International Journal of Advances in Applied Sciences (IJAAS) Volume 9, issue 4, Dec. 2020

ISSN: 2252-8814

IJAAS

International Journal of

Advances in Applied Sciences

Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science, engineering and technology areas, which cover topics including: industrial engineering, materials & manufacturing; mechanical, mechatronics & civil engineering; food, chemical & agricultural engineering; telecommunications, computer science, instrumentation, control, electrical & electronic engineering; and acoustic & music engineering.

Editor-in-Chief Qing Wang, Shandong University of Science and Technology, China

Managing Editors Chen-Yuan Chen, National Pingtung University of Education, Taiwan Guangming Yao, Harbin Normal University, China Habibolla Latifizadeh, West Virginia University, United States Md. Shakhaoath Khan, RMIT University, Australia Mohammad Hossein Ahmadi, Shahrood University of Technology, Iran

Editorial Board Members Aabha Jain, Prestige Institute of Engineering Management and Research, India Abdelhamid Bensafi, Abou Bekr Belkaid University of Tlemcen, Algeria Ajit Behera, National Institute of Technology Rourkela, India Alireza Heidari, California South University, United States Andrews Jeyaraj, Sathyabama Institute of Science and Technology, India B.V.S.N. Hari Prasad, Chaitanya Group of Colleges (Autonomous), India Bing Yang, China State Shipbuilding Corporation, China Eka Cahya Prima, Universitas Pendidikan Indonesia, Indonesia EL Mahdi Ahmed Haroun, University of Bahri, Sudan Fardin Dashty Saridarq, Technische Universiteit Eindhoven, Netherlands Guruswamy Revana, Jawaharlal Nehru Technological University, India Ho Soon Min, INTI International University, Malaysia K.V.L.N. Acharyulu, Bapatla Engineering College, India Kewen Zhao, Qiongzhou University, China Kirk R. Smith, University of California, United States Laith Ahmed Najam, College of Science Mosul University, Iraq Matthew Vechione, The University of Texas at El Paso, United States Mu-Song Chen, Da-Yeh University, Taiwan, Province of China Özen Özer, Kırklareli University, Turkey Prabang Setyono, University of Sebelas Maret, Indonesia Rutuja Shivraj Pawar, Yeshwantrao Chavan College of Engineering, India Shuaichen Ye, Beijing Institute of Technology, China Shubham Sharma, Punjab Technical University, India Siamak Hoseinzadeh, Islamic Azad University West Tehran Branch, Iran Totok R Biyanto, Sepuluh Nopember Institute of Technology, Indonesia Vahab Ghalandari, Shahid Bahonar University of Kerman, Iran VPS Naidu, National Aerospace Laboratories India, India Waleed Khalil Ahmed, United Arab Emirates University, United Arab Emirates Yee-Loo Foo, Multimedia University, Malaysia Yonathan Asikin, University of the Ryukyus, Japan

Published by:

Institute of Advanced Engineering and Science (IAES) Website: http://ijaas.iaescore.com Email: ijaas.iaes@gmail.com, info@iaesjournal.com

Information for Authors International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of Applied Sciences, Engineering and Technology. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science, engineering and technology areas, which cover topics including: industrial engineering, materials & manufacturing; mechanical, mechatronics & civil engineering; food, chemical & agricultural engineering; telecommunications, computer science, instrumentation, control, electrical & electronic engineering; and acoustic & music engineering.

Submission of a manuscript implies that it contains original work and has not been published or submitted for publication elsewhere. It also implies the transfer of the copyright from the author to the publisher and gives permission to reproduce any published material.

Paper Submission You must prepare and submit your papers as word document (DOC or DOCX) file format.

For more detailed instructions please take a look and download at: http://ijaas.iaescore.com/index.php/IJAAS/about/submissions#onlineSubmissions You can download the IJAAS template at: http://iaescore.com/gfa/ijaas.docx Manuscript must be submitted through our on-line system: http://ijaas.iaescore.com Once a manuscript has successfully been submitted via the online submission system authors may track the status of their manuscript using the online submission system.

The manuscript will be subjected to a full review procedure and the decision whether to accept it will be taken by the Editor based on the reviews.

Ethics in publishing For information on Ethics in publishing and Ethical guidelines for journal publication (including the necessity to avoid plagiarism and duplicate publication) see: http://ijaas.iaescore.com/index.php/IJAAS/about/editorialPolicies#sectionPolicies

Peer Review Process This journal operates a conventional single-blind reviewing policy in which the reviewer's name is always concealed from the submitting author. Authors should present their papers honestly without fabrication, falsification, plagiarism or inappropriate data manipulation. Submitted papers are evaluated by anonymous referees for contribution, originality, relevance, and presentation.

Papers will be sent for anonymous review by at least two reviewers who will either be members of the Editorial Board or others of similar standing in the field. In order to shorten the review process and respond quickly to authors, the Editors may triage a submission and come to a decision without sending the paper for external review. The Editor shall inform you of the results of the review as soon as possible, hopefully in 8 weeks. The Editors’ decision is final and no correspondence can be entered into concerning manuscripts considered unsuitable for publication in this journal. All correspondence, including notification of the Editors’ decision and requests for revisions, will be sent by email.

IJAAS

International Journal of

Advances in Applied Sciences An experimental study on performace of starch extracted from wheat flour as filtration control agent in drilling fluid Raheel Iqbal, Fawad Pirzada, Muhammad Zubair, Ameer Mehmood

255-260

Real power loss reduction by arctic char algorithm Lenin Kanagasabai

261-264

Chaotic based Pteropus algorithm for solving optimal reactive power problem Lenin Kanagasabai

265-269

Implementation of anti-collision train prototype based on arduino microcontroller Ahmed R. Ibrahim, Ziad M. Abood

270-275

Design of frequency selective surface comprising of dipoles using artificial neural network Monojit Rudra, P Soni Reddy, Rajatsubhra Chakraborty, Partha Pratim Sarkar

276-283

Development of software defect prediction system using artificial neural network Olatunji B. L., Olabiyisi S. O., Oyeleye C. A., Sanusi B. A., Olowoye A. O., Ofem O. A.

284-293

Application of thermal imaging for detecting cold air leak location in cold storage P. Pathmanaban, Shanmuga Sundaram Anandan, B. K. Gnanavel, C. P. Murigan

294-301

Solar energy as a potential contributor to help bridge the gap between electricity supply and growing demand in Iraq: A review Sulala Al-Hamadani

302-312

Efficiency of bond graph and external model integration for alarm processing of a central air conditioning system Abderrahmene Sallami, Dhia Mzoughi, Hatem Allagui, Abdelkader Mami

313-325

Powerful processing to three-dimensional facial recognition using triple information Mohammad Karimi Moridani, Ahad Karimi Moridani, Mahin Gholipour

326-332

Responsibility of the contents rest upon the authors and not upon the publisher or editors

IJAAS

Vol. 9

No. 4

pp. 225-332

December 2020

ISSN 2252-8814

International Journal of Advances in Applied Sciences (IJAAS) Vol.9, No. 4, December2020, pp. 255~260 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp255-260



255

An experimental study on performace of starch extracted from wheat flour as filtration control agent in drilling fluid Raheel Iqbal, Fawad Pirzada, Muhammad Zubair, Ameer Mehmood Institute of Petroleum & Natural Gas, Mehran University of Engineering & Technology, Pakistan

Article Info

ABSTRACT

Article history:

The phenomenon of lost of mud filtrate into a porous permeable formation due to high hydrostatic pressure compared to the formation pressure is known as fluid loss. This cause some major problems in well during drilling as poor cementing job, pipe stuck, and formation damage. Thus, to safe the well from such problems and in order to make safe and effective drilling an additive from wheat flour is extracted which is starch, and acting as a fluid loss control agent. The purpose of this research is to investigate the potential of utilizing this additive to form environmentally safe, non-toxic, high biodegradability and low-cost water-based drilling fluid samples with varying the amount of starch. Experimental results showed that Efficiency of starch obtained from wheat-flour is showing increment in rheological properties as compare to starch present in market by using same and varying quantity of both and observed that wheat-flour starch is more efficient as compare to starch in market. On the other hand, the efficiency of starch is good but it has been also improved by the extraction of starch from wheatflour by the centrifugation process.

Received Dec 23, 2019 Revised Feb 2, 2020 Accepted Jun 5, 2020 Keywords: Drilling fluid Filtration control Fluid loss Rheological properties Wheat flour

This is an open access article under the CC BY-SA license.

Corresponding Author: Raheel Iqbal, Institute of Petroleum & Natural Gas Engineering, Mehran University of Engineering & Technology, Indus Hwy, Jamshoro, Sindh 76062, Pakistan.

Email: 15PG46@students.muet.edu.pk | raheelarain66@gmail.com

1.

INTRODUCTION The Drilling fluid has the obligatory properties like carrying out rock cutting towards the surface, cleaning & cooling the bit, decreasing resistive forces, stabilizing wellbore & to prevent fluids that flow from pores into the borehole. The various methods for designing suitable drilling muds are developed for avoiding complexity of problems encountered during drilling operation. Since the initial operations that were executed in USA (which consist of clays and water for use) to complicated blends of distinct particular natural &artificial materials which are currently used. The drilling mud should be user friendly, cost effective and economically viable. Therefore, drilling muds are basically formulated to decrease the effect of damage and to ensure the possibility and economically viability of rotary drilling in hydrocarbon containing formations.

The filter cakes which are formed after the intrusion of drilling mud in the pore space of pay zone are compressible and they contain variable porosity and permeability characteristics, with low void spaces at the filter channel surface and maximum void spaces on cake surface [1]. In order to reduce filtrate invasion, fluid loss additives such as organic polymers are used which prohibit water invasion into formation. During the formulation of a mud in addition to microscopic structure and composition of filter cake related to it and the information of the characteristics of filtration, are of main importance [2]. During drilling and completion varying drilling muds in the bore-hole are used. The most significant factor for the increasing the production Journal homepage: http://ijaas.iaescore.com

256



ISSN: 2252-8814

is the physical and chemical compatibility of the mud with the reservoir rock. By the formation damage these muds can reduce the productivity of the well by invasion. Consequently, those additives are used i.e. CaCO3, which can reduce the chances of these damages in the formations by forming a filter cake of low permeability (optimum thickness) that reduces further invasion of solids and filtrate in the pore spaces of rock. After drilling these cakes are washed by for maximizing the flow in the wellbore [3].

Fluid loss and viscosity of mud are the important factors which must be investigated throughout the drilling of a well. For that mud is treated with several types of additives i.e. different polymers and chemicals, to achieve the requirements important for the particular well such as rheology, control of fluid loss, and weight of mud etc. Starch and calcite are the most important materials used to control fluid loss and to increase the weight of mud by forming mud cake respectively [4].

2. RESEARCH METHOD 2.1. Drilling fluid preparation and properties Few of the additives along with Barite BaSO4 and Calcite CaCO3 are most commonly used in waterbased drilling mud. Mainly three main factors which influenced the performance of drilling fluid are i.e., density, viscosity and filtration of drilling fluid. Therefore, more consideration for these factors during experimental work for the preparation of water-based fluid samples should be paid [4].

The fluid samples preparation at laboratory scale is obtained by the applying this methodology that any material of 1 g added to a 350 ml laboratory barrel at the standard level, that is relevant to addition of 1 pound of material to a fluid of 1 barrel.

This study is involved the preparation of four water-based drilling fluid samples which contain Bentonite as a filtration controller and viscosifier, for pH control caustic soda is used, and barite is used as a weighting agent. Along with them soda ash and Xanthan gum are also used as a hardness and rheology control materials respectively. The composition of all these additives is constant for all prepared samples except starch.

Then the most important is the concentration of starch as a filtration control agent is used to prepare samples of densities of 10.5 PPG. One of the samples labeled as sample no.1 is based on pure starch and the rest of the three samples are based on starch extracted from wheat-flour with varying amount as the compositions of prepared samples shown in Table 1.

Table 1. Drilling mud samples composition Product Water (ml) Bentonite (g) Barite (g) CaCO3 Starch (pure) (g) Starch (Wheat-Flour) (g) Caustic Soda (g) Soda Ash (g) Xanthan Gum (g)

1 325.5 24.5 101 0.40 0.20 0.25 1.00

Per lab barrel (350 ml) Sample number 2 3 325.5 325.5 24.5 24.5 101 101 0.40 0.35 0.20 0.20 0.25 0.25 1.00 1.00

4 325.5 24.5 101 0.45 0.20 0.25 1.00

2.2. Density of mud Density of the mud is the main parameter to consider during study and before the drilling process as it directly affects the properties of filter cake i.e. damage to the formation and filtrate loss.

An apparatus named as mud balance is used to choose such weight of mud in ppg that cause ceramic disk to less damage. The most common additive to increase the weight of mud in production zones is mostly calcite (CaCO3) and barite (BaSO4) to a less extent used in drilling fluids in hydrocarbon industries [5].

Four drilling mud samples by formulating both starches are prepared in this study to achieve the density range of 10.5 ppg. The amount use for the formulation of pure starch-based sample is 0.40 g, and the amount for the formulation of wheat-flour based samples ranges from 0.35 – 0.45 g.

2.3. Drilling fluid rheological properties The flow of matter or the fluids deformation is involved in the study of rheology. The importance of rheology is acknowledged during the fluid flow (velocity profiles) analysis, viscosities of fluids (apparent, plastic, and marsh funnel viscosities), cleaning the annular borehole and the losses of friction pressure.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 255 – 260

Int J Adv Appl Sci



ISSN: 2252-8814

257

The basis for all the investigations including hydraulics of wellbore and to evaluate the mud system functionality is the rheological properties. Gel strength and yield points of the fluids are also included in rheological properties. Mud rheological properties i.e. Gel, viscosities, yield point, and density are continuously tested throughout the operation of drilling a well. Mud rheological properties are very critical to maintain and control during the failure due to result in time, financial loss, and in excessive cases, the result is in the abandonment of the well. Filtration, pH, chemical analysis (alkalinity and lime content, chloride, calcium, etc.), and resistivity apart from rheology are also tested throughout the drilling of a well [6].

In drilling fluid laboratories as well as on rig site, rotational viscometer is frequently used to measure the rheological properties of mud.

The readings are taken on 600, 300, 200, 100, 60, 30 and 6 rpm (rotation per minute). Later these readings are plotted on a chart of shear stress and shear rate which are used to determine viscosity and appropriate viscosity model.

Rotational viscometer also provides the information about other rheological properties, which include apparent, plastic, and effective viscosities (µa, µp, and µe), gel strength (Gel) and yield point (YP) as shown in Table 2.

Table 2. Rheological properties of mud sample Sample Number Plastic Viscosity (cp) Apparent Viscosity (cp) Yield Point (Ib/100 ft2) Gel Strength @ 10 min (Ib/100 ft2) Ø600 Ø300 Ø200 Ø100 Ø60 Ø30 Ø6

1 18 32.75 29.5 15 65.5 47.5 40 31 27 23 17

2 25 40 30 17.3 80 55 46 35.5 30 25 19

3 27 37.5 21 15.50 75 48 40.50 32 27.50 23.50 17

4 24.5 42 35 19.25 84 59.5 50.25 39 34 27 20

Given equations are utilized for these purposes: Apparent Viscosity (µa), (cp) =

∅600 2

Plastic Viscosity (µp), (cp) =ø600 – ø300 Effective Viscosity (µe), (cp) =

300 𝑥 ∅ 𝜔

(1) (2) (3)

Yield Point (Yp), (lb/100 ft2) = ∅300 − µ𝑝

(4)

Shear Stress (τ), (lb/100 ft2) = 1.065 * Ø

(5)

Shear Rate (ɣ), sec-1 = 1.7023 * ω

(6)

Where, Ø indicates the reading of dial, lb/100 ft2 and ω indicates rotation of rotor speed, rpm.

2.4. Bingham plastic fluid To identify the properties related to flow for the different mud types Bingham plastic model is a basic two-parameter model used in drilling industry widely. It is known to as most common model for fluid to estimate the non-Newtonian fluids rheology. Shear stress is a straight-line function of shear rate which is the basic supposition of this model. Yield point or also named as threshold stress is the point where shear rate is zero. By the reduction in colloidal solids plastic viscosity (PV) is achieved best and as low as possible for drilling fast.

While, to carry cuttings out of the hole, yield point (YP) should be high enough, but not as much that pressure of pump would be unwanted when flow of mud initiated. On the behalf of treatment for mud adjustments in YP is done. For the both low and high shear rates range Bingham plastic model have its own limitations. The physical/solid reason behind this behaviour is that the liquid generally contains particles (clay) or large molecules (polymers) which generally have some kind of interaction, while creating a weak An experimental study on performace of starch extracted from wheat flour as filtration… (Raheel Iqbal)

258



ISSN: 2252-8814

solid structure, formerly known as a false body, and at that point a certain amount of stress is required to break this structure. Under viscous forces the particles tend to move as the once that structure has been broken. Again, the particles are linked, by removing that stress [7].

Those results which are acceptable for a drilling mud diagnosis are produced by Bingham plastic model. But, for hydraulic calculations its accuracy is not that much. A Bingham body doesn’t begin to flow until a shearing stress, corresponding to the yield value, is exceeded. The results for Bingham Plastic Model are obtained by plotting graph between shear stress and shear rate as shown in Figure 1, which are calculated by using the shear stress and shear rate (5) and (6).

Figure 1. Bingham plastic model of samples

2.5. Drilling fluid ph determination The importance of pH is cleared by knowing this that it affects the solubility of the organic thinners, contaminant removal, corrosion mitigation and the dispersion of clays presents in the mud. For hydrogen ion concentration representation of a mud the vital role is played by pH. pH is mostly used to express the drilling fluid especially water- based mud acidity or alkalinity. Generally, a numerical value ranges from 0 – 14 are used for the representation of pH of any fluid which indicates that the hydrogen concentration in the fluid is an inverse measurement [4]. pH is expressed by the equation given follow: pH = - log10[H]

(7)

Where, H is the hydrogen ion concentration in mol.

The pH value decreases according to the formulae as the acidity of the fluid increases by the addition of more hydrogen atoms. Generally, pH of the fluid is considered at 7. Above the reading 7 indicates the alkaline pH of any of the fluid. And the pH below 7 referred to acidic pH. In drilling mud there are main three chemical components involved which are (OH -) hydroxyl ions, carbonate ions (CO3-2) and alkalinity of drilling mud including (HCO-) bicarbonates ions [8].

The acidity reduced by ions are represented and known as the alkalinity. So for the better measurement of pH, it is observed that pH meter is mostly used rather than the use of a pH paper because of that pH meter measured the accurate figures values. However, it is ensured that the pH meter is calibrated accurately.

3. RESULTS AND DISCUSSIONS 3.1. Characteristics of drilling fluids Achieved drilling fluid samples densities of four samples of both starch’s based are 10.5 PPG as shown in Figure 2. Prepared four samples of mud with rheological properties which are obtained in this study based on different filtration controllers are shown in Table 2.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 255 – 260

Int J Adv Appl Sci

ISSN: 2252-8814



259

Figure 2. Amount of starch vs densities After an accurate look to the measured values of prepared samples, the observations are that the prepared drilling fluid samples rheological properties based on different filtration control agents are showing the variation in obtained results such as plastic viscosity, apparent viscosity, yield point, and gel strength of all samples are different. But it is observed that sample no.2 with amount of 0.40 g of starch from wheat- flour when compared with the sample no. 1 with amount of 0.40 g of pure starch, all the obtained results of wheat-flour based starch sample is showing better apparent and plastic viscosities including good yield point and gel strength as shown in Table 2.

3.2. Bingham plastic model It is discussed that fluids which are non-Newtonian exhibit a relationship between the shear rate and shear stress measured for the formulated samples as shown in Figure 1.

According to the graphs which are plotted for four water- based drilling fluid samples representing that the fluid samples of 10.5 ppg are showing almost varying trend except sample no.1 and sample no. 3 which are same at some extent and then variation occurred also between them as shown in Figure 1.

A general trend line is drawn for the Bingham plastic fluid and it is observed that the two different filtration control fluid samples exhibit much higher shear stress and a yield point of 35 Ib/100 ft2 is obtained as line cutting the vertical axis as shown in Figure 1. For wheat flour starch-based sample no. 4 and it is also discussed in Table 2 as compare to the pure starch-based sample no. 1 and other samples of wheat-flour starch.

3.3. pH determination pH of the prepared sample is determined by using pH meter and the obtained results are listed as shown in Figure 3. If a comparison is generated between both starch-based samples which include pure starch and starch from wheat-flour, it is observed that there is a difference between pH of all samples. But it is also observed that the sample no.1 which contains pure starch with amount of 0.40 g is showing a pH value almost similar to the sample no. 3 with amount of 0.35 which is based on wheat-flour starch as shown in Figure. 3.

Figure 1. pH of mud samples An experimental study on performace of starch extracted from wheat flour as filtration… (Raheel Iqbal)

260



ISSN: 2252-8814

Though, it is also observed that wheat-flour starch is efficient in pH study, because of the reason that sample no.1 contains amount of starch 0.40 g and pH value 9.25, and at the same time sample no.3 contain amount of starch 0.35 g and pH value 9.27.

4.

CONCLUSION As this study is related the investigation about the effect and efficiency of starch derived from wheat flour to prepare the samples of water-based drilling fluid and compare their results with the samples which are prepared by using same composition of starch present in market and used in industry. On the basis of laboratory measurements analysis and interpretation, the main concluded and recommended points are listed: − Mud weight of 10.5 lb/gal which is optimum for the X well was selected among the five prepared mud densities, considering that it can sustain the formation pressure. By using another mud weight the formation starts to create fractures in the well by the effect of mud weight density. − Efficiency of starch obtained from wheat-flour is showing increment in rheological properties as compare to starch present in market by using same and varying quantity of both and observed that wheat-flour starch is more efficient as compare to starch in market. − On the other hand, the efficiency of starch is good but it will be also improved by the extraction of starch from wheat-flour by the centrifugation process. − It is also concluded that less the amount of wheat flour starch in drilling fluid then higher will be the pH value.

ACKNOWLEDGEMENTS We would like to acknowledge institute of Petroleum & Natural Gas Engineering of Mehran UET Jamshoro for their incredible support in our research.

REFERENCES [1] Khaled J. Hassiba & Mahmood Amani, “The effect of salinity on the rheological properties of waterbased mud under high pressures and high temperatures for drilling offshore and deep wells,” Earth Science Research, vol. 2, no. 1, 2013.

[2]

Khaled Elshreef & Aref Lashin,“Investigation of mud density and weighting materials effect on drilling fluid filter cake properties and formation damage,”Journal of African Earth Sciences, Feb 2016.

[3]

Prasenjit Talukdar and Subrata Borgohain Gogoi, “Use of Calcium Carbonate as bridging and weighting agent in the non-damaging drilling fluid for some oilfields of Upper Assam Basin,” International Journal of Current Research, vol. 7, no. 08, pp.18964-18981, Aug2015.

Hudson, T. & Coffey, M. “Fluid loss control through the use of a liquid thickened completion and work over brine,” Journal of Petroleum Technology, vol. 35, no. 10, pp. 1776-1782, 1983.

Majid Sajjadian, Ehsan Esmaeil pour Motlagh, Ali Akbar Daya, “Laboratory investigation to use lost circulation material in water base drilling fluid as lost circulation pills,“ International Journal of Mining Science (IJMS), vol 2, no. 1, pp. 33-38, 2016.

Nediljka Gaurina-Medimurec, “Laboratory Evaluation of Calcium Carbonate particle size selection for drill-in fluids,” Rudarsko-gcolofko-naftnizbomik, vol. 14, no. 14, 2002.

Aydin Od abasi, “An experimental study of particle size and concentration effects of Calcium Carbonate on Rheological and Filtration properties of drill-in fluids,” In partial fulfillment of the requirements for the degree of Master of Science in Petroleum and Natural Gas Engineering Department Middle East Technical University, May 2015.

Samavati R., Abdullah N., Tahmasbi Nowtarki K., Hussain S. A., and Awang Biak D. R., “Rheological and fluid loss properties of water-based drilling mud containing hcl-modified fufu as a fluid loss control agent,” International Journal of ChemicalEngineering and Applications, vol. 5, no. 6, Dec 2014.

[4] [5]

[6] [7]

[8]

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 255 – 260

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 261~264 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp261-264



261

Real power loss reduction by arctic char algorithm Lenin Kanagasabai Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India

Article Info

ABSTRACT

Article history:

This work presents Arctic Char Algorithm (ACA) for solving optimal reactive power problem. In North America movement of Arctic char phenomenon is one among the twelve-monthly innate actions. Deeds of Arctic char have been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Previous to the movement, Arctic char come to a decision about the passageway based on their perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary solution) in mutual pathway (evolutionary operators). Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.

Received Jan 3, 2020 Revised Mar 4, 2020 Accepted Jun 8, 2020 Keywords: Arctic char Optimal reactive power Transmission loss

This is an open access article under the CC BY-SA license.

Corresponding Author: Lenin Kanagasabai, Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India.

Email: gklenin@gmail.com

1.

INTRODUCTION Reactive power problem plays a key role in secure and economic operations of power system.

Optimal reactive power problem has been solved by variety of types of methods [1-6]. Nevertheless, numerous scientific difficulties are found while solving problem due to an assortment of constraints.

Evolutionary techniques [7-16] are applied to solve the reactive power problem, but the main problem is many algorithms get stuck in local optimal solution & failed to balance the Exploration & Exploitation during the search of global solution. This work presents Arctic Char Algorithm (ACA) for solving optimal reactive power problem. In North America movement of Arctic char phenomenon is one among the twelvemonthly innate actions. Through mountain streams millions of Arctic chars will move for spawning. During the movement starving bears, human fishers and waterfalls are generally significant threats they have to face.

In this work deed of Arctic char has been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.

2.

PROBLEM FORMULATION Objective of the problem is to reduce the true power loss: 𝐅 = 𝐏𝐋 = ∑𝐤∈𝐍𝐛𝐫 𝐠 𝐤 (𝐕𝐢𝟐 + 𝐕𝐣𝟐 − 𝟐𝐕𝐢 𝐕𝐣 𝐜𝐨𝐬𝛉𝐢𝐣 )

Journal homepage: http://ijaas.iaescore.com

(1)

262



ISSN: 2252-8814

Voltage deviation given as follows: 𝐅 = 𝐏𝐋 + 𝛚𝐯 × 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧

(2)

Voltage deviation given by: 𝐍𝐩𝐪

𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧 = ∑𝐢=𝟏 |𝐕𝐢 − 𝟏|

(3)

Constraint (Equality) 𝐏𝐆 = 𝐏𝐃 + 𝐏𝐋

(4)

Constraints (Inequality) 𝐦𝐢𝐧 𝐦𝐚𝐱 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 ≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 ≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤

(5)

𝐦𝐚𝐱 𝐐𝐦𝐢𝐧 𝐠𝐢 ≤ 𝐐𝐠𝐢 ≤ 𝐐𝐠𝐢 , 𝐢 ∈ 𝐍𝐠

(6)

𝐕𝐢𝐦𝐢𝐧 ≤ 𝐕𝐢 ≤ 𝐕𝐢𝐦𝐚𝐱 , 𝐢 ∈ 𝐍

(7)

𝐓𝐢𝐦𝐢𝐧 ≤ 𝐓𝐢 ≤ 𝐓𝐢𝐦𝐚𝐱 , 𝐢 ∈ 𝐍𝐓

(8)

Qmin ≤ Q c ≤ Qmax , i ∈ NC c C

(9)

3.

ARCTIC CHAR ALGORITHM In North America movement of Arctic char phenomenon is one among the twelve-monthly innate actions. Through mountain streams millions of Arctic chars will move for spawning. During the movement starving bears, human fishers and waterfalls are generally significant threats they have to face. In this work deed of Arctic char has been imitated to design the algorithm.

In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Solutions are initialized arbitrary mode with reverence to the solution space. 𝑃𝑟𝑒𝑙𝑖𝑚𝑖𝑛𝑎𝑟𝑦 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 = 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 + 𝑟𝑎𝑛𝑑𝑜𝑚 ∗ (𝑢𝑝𝑝𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 − 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑)

(10)

Previous to the movement, Arctic char come to a decision about the passageway based on their perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary solution) in mutual pathway (evolutionary operators). 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 ∶ {

𝑁𝐴1 = [𝜇 ∗ 𝐴𝐶𝑠 ] 𝑁𝐴2 = 𝐴𝐶𝑠 − 𝑁𝐴1

(11)

Where 𝑁𝐴1 is the total number of Arctic char groups which pass through ocean and ponds, 𝑁𝐴2 is the total number of Arctic char group transit through forest area and mountain valley, 𝐴𝐶𝑠 is the total number of Arctic char groups which partake in the relocation and 𝜇 is Distribution factor is symbolizes the Arctic char intuition.

Crossing of ocean, lakes and ponds has been scientifically modeled as, {

𝑌𝑁 = 𝑌𝐹 + 𝛿(𝑡, (𝑢𝑏 − 𝑌𝐹 )) 𝑌𝑁 = 𝑌𝐹 + 𝛿(𝑡, (𝑦𝐹 − 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑))

(12)

Where t symbolizes the present iteration number, 𝑌𝑁 symbolize a new-fangled perceive region (fresh solution) and 𝑌𝐹 shows the previous area of the explore convey (preceding solution). 𝛿(𝑦, 𝑧) is computed by, 𝑦 𝑏

𝛿(𝑦, 𝑧) = 𝑧 ∗ 𝑟𝑎𝑛𝑑𝑜𝑚 ∗ (1 − ) 𝑇

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 261 – 264

(13)

Int J Adv Appl Sci



ISSN: 2252-8814

263

Where T indicates maximum number of iterations, b is an arbitrary number normally larger than value 1 Chief hunters discover the regions with a satisfactory Arctic char concentration (solution fitness).

Subsequent to that, they notify the engaged agent to make use of close by area to discover additional concentrated regions (solution with elevated fitness). This operation has been precisely defined by, 𝑌𝑅 = 𝛽 ∗ (𝑌𝑀1 − 𝑌𝑀2 ) + 𝑌𝑀1

(14)

Where β is a arbitrary number which span from 0 and 1 with consistent distribution, Y R stand for the freshly spotted solution by the engaged agent, YM1 is the solution acquired by the primary key hunter and YM2 is the solution acquired by the subsequent one.

Second operator simulates the bears hunt line of assault. Naturally they notify each other when they find an appropriate region. When they discover a region with superior Arctic char concentration, they notify to other bears. It expressed as, 𝑌𝐵 = 𝑐𝑜𝑠(𝜑) ∗ (𝐵𝑇𝑅 − 𝐶𝐿𝑅 ) + 𝐵𝑇𝑅

(15)

Where YB symbolizes the new-fangled spotted area, BTR is the most excellent accounted area by the hunting squad, CLR is the present area for which the bears have determined to execute the local exploitation and φ is a capricious angle with a segment on every side of 0 to 360 degrees. cos (φ) articulate the bears to their objective.

End of the repositioning, stay alive Arctic char will get together in their mark for spawning.

In Arctic char this normal event is replicated through a compilation stem. After Arctic char surpass through their passageway (operator’s performance), the Arctic char subgroups (solutions) are composed in a exclusive stem. Solutions are extorted from mutual operators and make an exclusive population. At this condition, the proposed algorithm has reached the ending stage of the primary iteration.

Step1. Determine the population size, solution space, total number of variables, iterations Step2. Regulate the Arctic char subgroups chaotically Step3. Depending on migration 𝜇 passageway is chosen Step4.Compute the fitness of hunted Arctic char Step5. Arctic char used for spawn has been rumple together Step6. If yes obtain the global solution or else go to step number 3.

4.

SIMULATION RESULTS At first in standard IEEE 14 bus system the validity of the proposed Arctic Char Algorithm (ACA) has been tested & comparison results are presented in Table 1.

Table 1. The validity of the proposed Arctic Char Algorithm (ACA) Control variables V1 V2 V3 V6 V8 Q9 T56 T47 T49 Ploss (MW)

ABCO [17] 1.06 1.03 0.98 1.05 1.00 0.139 0.979 0.950 1.014 5.92892

IABCO [17] 1.05 1.05 1.03 1.05 1.04 0.132 0.960 0.950 1.007 5.50031

ACA 1.00 1.05 1.03 1.01 0.90 0.100 0.900 0.900 1.000 4.1652

Then IEEE 300 bus system [18] is used as test system to validate the performance of the Arctic Char Algorithm (ACA). Table 2 shows the comparison of real power loss obtained after optimization.

Table 2. Comparison of real power loss Parameter PLOSS (MW)

Method EGA [19] 646.2998

Method EEA [19] 650.6027

Method CSA [20] 635.8942

ACA 616.2596

Real power loss reduction by arctic char algorithm (Lenin Kanagasabai)

264



ISSN: 2252-8814

5.

CONCLUSION In this work Arctic Char Algorithm (ACA) successfully solved the optimal reactive power problem.

Deeds of Arctic char have been imitated to design the algorithm. End of the repositioning, stay alive Arctic char will get together in their mark for spawning. In Arctic char this normal event is replicated through a compilation stem. After Arctic char surpass through their passageway (operator’s performance), the Arctic char subgroups (solutions) are composed in a exclusive stem. Solutions are extorted from mutual operators and make an exclusive population. Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.

REFERENCES [1] [2] [3] [4] [5] [6]

[7] [8] [9]

[10] [11]

[12] [13] [14] [15] [16] [17]

K. Y. Lee., “Fuel-cost minimisation for both real and reactive-power dispatches,” Proceedings Generation, Transmission and Distribution Conference, vol. 131, no. 3, pp. 85-93, 1984.

N. I. Deeb., “An efficient technique for reactive power dispatch using a revised linear programming approach,” Electric Power System Research, vol. 15, no. 2, pp. 121–134, 1998.

M. R. Bjelogrlic, M. S. Calovic, B. S. Babic.,”Application of Newton’s optimal power flow in voltage/reactive power control,” IEEE Trans Power System, vol. 5, no. 4, pp. 1447-1454, 1990.

S. Granville., “Optimal reactive dispatch through interior point methods,” IEEE Transactions on Power System, vol. 9, no. 1, pp. 136–146, 1994.

N. Grudinin., “Reactive power optimization using successive quadratic programming method,” IEEE Transactions on Power System, vol. 13, no. 4, pp. 1219–1225, 1998.

Wei Yan, J. Yu, D. C. Yu , K. Bhattarai., ”A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method,” IEEE Trans. Pwr. Syst.,vol. 21, no.1, pp. 61-67, 2006.

Aparajita Mukherjee, Vivekananda Mukherjee, “Solution of optimal reactive power dispatch by chaotic krill herd algorithm,” IET Gener. Transm. Distrib, vol. 9, vol. 15, pp. 2351–2362, 2015.

Hu, Z., Wang, X. & Taylor., “Stochastic optimal reactive power dispatch: Formulation and solution method,” Electr. Power Energy Syst., vol. 32, pp. 615-621, 2010.

Mahaletchumi A/P Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman,Mahfuzah Mustafa and Rosdiyana Samad, “Multi-objective evolutionary programming (MOEP) using mutation based on adaptive mutation operator (AMO) applied for optimal reactive power dispatch,” ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 14, 2016.

Pandiarajan, K. & Babulal, C. K.,“ Fuzzy harmony search algorithm based optimal power flow for power system security enhancement,” International Journal Electric Power Energy Syst., vol. 78, pp. 72-79, 2016.

Mahaletchumi Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman, Mahfuzah Mustafa, Rosdiyana Samad, “Benchmark studies on optimal reactive power dispatch (ORPD) based multi-objective evolutionary programming (MOEP) using mutation based on adaptive mutation adapter (AMO) and polynomial mutation operator (PMO),” Journal of Electrical Systems, vol. 12, no. 1, pp. 121-132, 2016.

Rebecca Ng Shin Mei, Mohd Herwan Sulaiman, Zuriani Mustaffa, “Ant lion optimizer for optimal reactive power dispatch solution,” Journal of Electrical Systems, Special Issue AMPE2015, pp. 68-74, 2016.

Gagliano A., Nocera F., “Analysis of the performances of electric energy storage in residential applications,” International Journal of Heat and Technology , vol. 35, Special Issue 1, pp. S41-S48, 2017.

Caldera M., Ungaro P., Cammarata G., Puglisi G., “Survey-based analysis of the electrical energy demand in Italian households,” Mathematical Modelling of Engineering Problems, vol. 5, no. 3, pp. 217-224, 2018.

Puris.A, Bello, R., Molina, D. & Herrera, F. “Variable mesh optimization for continuous optimization problems,” Soft Computing, vol. 16, no. 3, pp. 511-523, 2012.

Price.K, R. M. Storn, and J. A. Lampinen, Differential evolution: A practical approach to global optimization, Springer, 2006.

Chandragupta Mauryan Kuppamuthu Sivalingam, Subramanian Ramachandran, Purrnimaa Shiva Sakthi Rajamani, “Reactive power optimization in a power system network through metaheuristic algorithms,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 25, no. 6, pp. 4615-4623, 2017.

[18] IEEE, “The IEEE-test systems” Retreived from: http://www.ee.washington.edu/trsearch/pstca/, 1993.

[19] S.S. Reddy, et al., “Faster evolutionary algorithm based optimal power flow using incremental variables,” Electrical Power and Energy Systems, vol. 54, pp. 198-210, 2014.

[20] S. Surender Reddy, “Optimal reactive power scheduling using cuckoo search algorithm,” International

Journal of Electrical and Computer Engineering, vol. 7, no. 5, pp. 2349-2356, 2017.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 261 – 264

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 265~269 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp265-269



265

Chaotic based Pteropus algorithm for solving optimal reactive power problem Lenin Kanagasabai Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India

Article Info

ABSTRACT

Article history:

In this work, a Chaotic based Pteropus algorithm (CPA) has been proposed for solving optimal reactive power problem. Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay. To the original Pteropus algorithm chaotic disturbance has been applied and the optimal capability of the algorithm has been improved in search of global solution. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance is added at the time of stagnation. Furthermore, exploration and exploitation capability of the proposed algorithm has been improved. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced.

Received Jan 8, 2020 Revised May 31, 2020 Accepted Jun 9, 2020 Keywords: Chaotic Pteropus behaviour Optimal reactive power Transmission loss

This is an open access article under the CC BY-SA license.

Corresponding Author: Lenin Kanagasabai, Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India.

Email: gklenin@gmail.com

1.

INTRODUCTION To have secure & economic, operations of the power system optimal reactive power problem plays a prime role. Numerous conventional methods [1-6] have been successfully solved the problem. But difficulty found in handling the inequality constraints. Various types of evolutionary algorithms [7-18] applied to solve the problem. This paper projects chaotic Pteropus algorithm (CPA) for solving reactive power problem. Pteropus algorithm is designed based on the actions of Pteropus. while flying it avoid obstacles by using sonar echoes, particularly utilize time delay; happened while release and reflection of echo which has been utilized during the period of for course-plotting. In Projected algorithm echolocation feature is utilized in the algorithm and chaos theory intermingled in the flowing process. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance 𝑃𝑐 is added at the time of stagnation. Projected CPA algorithm has been tested in standard IEEE 14,300 bus systems & simulation study show the best performance of the projected algorithm in reducing the real power loss.

2.

PROBLEM FORMULATION The key objective of the reactive power problem is to minimize the system real power loss & given as, Ploss= ∑nk=1 g k(V2 +V2−2Vi Vj cos θ k=(i,j)

i

j

ij

)

Journal homepage: http://ijaas.iaescore.com

(1)

266



ISSN: 2252-8814

Voltage deviation magnitudes (VD) is stated as follows, Minimize VD = ∑nl k=1|Vk − 1.0|

(2)

Load flow equality constraints: PGi – PDi − Vi ∑nb

j=1 Vj

Q Gi − Q Di − Vi ∑nb

Gij [ +Bij

j=1 Vj

Gij [ +Bij

cos θij ] = 0, i = 1,2 … . , nb sin θij sin θij ] = 0, i = 1,2 … . , nb cos θij

(3)

(4)

Inequality constraints are: min max VGi ≤ VGi ≤ VGi , i ∈ ng

(5)

min max VLi ≤ VLi ≤ VLi , i ∈ nl

(6)

max Qmin Ci ≤ Q Ci ≤ Q Ci , i ∈ nc

(7)

max Qmin Gi ≤ Q Gi ≤ Q Gi , i ∈ ng

(8)

Timin ≤ Ti ≤ Timax , i ∈ nt

(9)

min max SLi ≤ SLi , i ∈ nl

(10)

3.

PTEROPUS ALGORITHM Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay; happened while release and reflection of echo which has been utilized during the period of for course-plotting. Generalized rules for Pteropus algorithm are: a. To sense the distance- all Pteropus use echolocation b. In arbitrarily mode Pteropus fly with velocity ϑi at position yi with a fixed frequencyfmin , varying wavelength λ and loudness A0 to search for prey. They can robotically adjust the frequency of their released pulses and regulate the rate of pulse emission r ∈ [0; 1], with reference to the propinquity of the goal. c. Loudness will vary from a large (positive) A0 to a minimum constant value Amin .

Pteropus algorithm Initialize the population Pulse frequency defined in the range of Gi ∈ [Q min , Gmax ] ri ,Ai are defined While (t <Tmaximum) By adjustment of frequency new solutions are generated Obtained Solution & velocity are updated If (random (0; 1) > ri ) Form the solution best one is selected Around the best solution – a local solution will be engendered End if In arbitrary mode new solutions are generated If (random (0; 1) < Ai and f (yi) < f(y)) New solutions are formed ri and Ai values are increased End if Current best is found by ranking the Pteropus in order End while Output the optimized results Virtual Pteropus are moved to form new solutions by the following, Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 265 – 269

Int J Adv Appl Sci

ISSN: 2252-8814

(t)

Gi = Gmin + (Gmax − Gmin ) ∪ (0,1), (t+1)

li

(t+1)

yi

(t)

= lti + (yit − best)Gi , (t)

(t)

= yi + l i



267 (11) (12) (13)

Existing finest solution has been modified by the following, (t) y (t) = best + ϵAi (2U(0,1) − 1),

(14)

When ri increases, Ai will decrease; when a Pteropus finds a prey & it mathematically written as follows, (t+1) (t) (t) (0) Ai = αAi , ri = ri [1 − exp(−γϵ)], (15) To improve the Pteropus algorithm chaotic disturbance [19-21] is introduced. Here, variance 𝜎 2 demonstrates the converge degree of all particles. 𝜎 2 = ∑𝑁 𝑖=1[(𝑓𝑖 − 𝑓𝑎𝑣𝑔 )⁄𝑓 ]

2

(16)

𝑓 = 𝑚𝑎𝑥 {1, 𝑚𝑎𝑥{|𝑓𝑖 − 𝑓𝑎𝑣𝑔 |}}

(17)

𝑦𝑖𝑑 (𝑡 + 1) = 𝜇𝑦𝑖𝑑 (𝑡)(1 − 𝑦𝑖𝑑 (𝑡))

(18)

In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance 𝑃𝑐 is added at the time of stagnation. Thus, 𝑃𝑐 𝑖𝑠 𝑚𝑜𝑑𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑃𝑐′ . ′ (𝑡 𝐸𝑐𝑑 + 1) = 𝑝𝑐𝑑 (𝑡) + 𝑍𝑖𝑑 (2𝑦𝑖𝑑 (𝑡) − 1)

(19)

𝑍𝑖𝑑 = 𝛽|𝑝𝑐𝑑 (𝑡) − 𝐸𝑖𝑑 (𝑡)|

(20)

Chaotic based Pteropus Algorithm Initialize the population Pulse frequency defined in the range of Gi ∈ [Gmin , Gmax ] ri ,Ai are defined While (t <Tmaximum) By adjustment of frequency new solutions are generated Obtained Solution & velocity are updated Using the equations update the velocities and locations (t) Gi = Gmin + (Gmax − Gmin ) ∪ (0,1), (t+1) (t) li = lti + (yit − best)Gi , (t+1)

(t)

(t)

yi = yi + l i If (random (0; 1) > ri ) Form the solution best one is selected Around the best solution – a local solution will be engendered End if In arbitrary mode new solutions are generated If (random (0; 1) < Ai and f (yi) < f(y)) New solutions are formed ri and Ai values are increased End if Current best is found by ranking the Pteropus in order End while Output the optimized results

Chaotic based Pteropus algorithm for solving optimal reactive power problem (Lenin Kanagasabai)

268



ISSN: 2252-8814

4.

SIMULATION RESULTS Proposed Chaotic based Pteropus algorithm (CPA) has been tested in standard IEEE 14,300 bus systems and comparison has been done with standard algorithms. Simulation output clearly indicates about the efficiency of the proposed algorithm in reducing the real power loss.

At first in standard IEEE 14 bus system the validity of the proposed CPA algorithm has been tested & comparison results are presented in Table 1.

Table 1. Comparison results Control variables V1 V2 V3 V6 V8 Q9 T56 T47 T49 Ploss (MW)

ABCO [22] 1.06 1.03 0.98 1.05 1.00 0.139 0.979 0.950 1.014 5.92892

IABCO [22] 1.05 1.05 1.03 1.05 1.04 0.132 0.960 0.950 1.007 5.50031

Projected CPA 1.03 1.00 1.01 1.00 0.99 0.129 0.969 0.948 1.002 5.49842

Then IEEE 300 bus system [23] is used as test system to validate the performance of the proposed CPA algorithm. Table 2 shows the comparison of real power loss obtained after optimization. Real power loss has been considerably reduced when compared to the other standard reported algorithms.

Table 2 comparison of real power loss Parameter PLOSS (MW)

Method EGA [24] 646.2998

Method EEA [24] 650.6027

Method CSA [25] 635.8942

Projected CPA 627.1564

5.

CONCLUSION In this paper, chaotic based Pteropus algorithm (CPA) has been successfully solved the optimal reactive power problem. Natural actions of Pteropus has been effectively imitated and modelled to solve the problem. An adaptive chaotic disturbance 𝑃𝑐 is added at the time of stagnation Performance of the Pteropus algorithm has been improved and better-quality solutions have been obtained. In addition, exploration and exploitation capability of the proposed algorithm has been enhanced. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced.

REFERENCES [1] [2] [3] [4] [5] [6]

[7] [8] [9]

K. Y. Lee, et al, “Fuel-cost minimisation for both real and reactive-power dispatches,” Proceedings Generation, Transmission and Distribution Conference, vol. 131, no. 3, pp. 85-93, 1984.

N. I. Deeb, et al., “An efficient technique for reactive power dispatch using a revised linear programming approach,” Electric Power System Research, vol. 15, no. 2, pp. 121-134, 1988.

M. R. Bjelogrlic, M. S. Calovic, B. S. Babic, et. al., “Application of Newton’s optimal power flow in voltage/reactive power control,” IEEE Trans Power System, vol. 5, no. 4, pp. 1447-1454, 1990.

S. Granville, “Optimal reactive dispatch through interior point methods,” IEEE Transactions on Power System, vol/issue: 9(1), pp. 136–146, 1994.

N. Grudinin, “Reactive power optimization using successive quadratic programming method,” IEEE Transactions on Power System, vol. 13, no. 4, pp. 1219-1225, 1998.

Wei Yan, J. Yu, D. C. Yu and K. Bhattarai, “A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method,” IEEE Trans. Pwr. Syst.,vol. 21,no. 1, pp. 61-67, 2006.

Aparajita Mukherjee, Vivekananda Mukherjee, “Solution of optimal reactive power dispatch by chaotic krill herd algorithm,” IET Gener. Transm. Distrib, vol. 9, no. 15, pp. 2351-2362, 2015.

Hu, Z., Wang, X. & Taylor, G. “Stochastic optimal reactive power dispatch: Formulation and solution method,” Electr. Power Energy Syst., vol. 32, no. 6, pp. 615-621, 2010.

Mahaletchumi A/P Morgan , Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman, Mahfuzah Mustafa and Rosdiyana Samad, “Computational intelligence technique for static VAR compensator (SVC) installation

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 265 – 269

Int J Adv Appl Sci

[10]

[11]

[12] [13]

[14]

[15]

[16] [17]

[18]

[19] [20] [21]

[22]

[23] [24] [25]

ISSN: 2252-8814



269

considering multi-contingencies (N-m),” ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 22, Dec 2015.

Mohd Herwan Sulaiman, Zuriani Mustaffa, Hamdan Daniyal, Mohd Rusllim Mohamed and Omar Aliman, “Solving optimal reactive power planning problem utilizing nature inspired computing techniques,” ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 21, pp.9779-9785, Nov 2015.

Mohd Herwan Sulaiman, Wong Lo Ing, Zuriani Mustaffa and Mohd Rusllim Mohamed, “Grey wolf optimizer for solving economic dispatch problem with valve-loading effects,” ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 21, pp. 9796-9801, Nov 2015.

Pandiarajan, K. & Babulal, C. K., “Fuzzy harmony search algorithm based optimal power flow for power system security enhancement,” International Journal Electric Power Energy Syst., vol. 78, pp. 72-79. 2016.

Mustaffa, Z., Sulaiman, M.H., Yusof, Y., Kamarulzaman, S.F., “A novel hybrid metaheuristic algorithm for short term load forecasting,” International Journal of Simulation: Systems, Science and Technology, vol. 17, no. 41, pp. 6.1-6.6, 2017.

Sulaiman, M.H., Mustaffa, Z., Mohamed, M.R., Aliman, O., “An application of multi-verse optimizer for optimal reactive power dispatch problems,” International Journal of Simulation: Systems, Science and Technology, vol. 17, no. 41, pp. 5.1-5.5, 2017.

Mahaletchumi A/P Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman,Mahfuzah Mustafa and Rosdiyana Samad, “Multi-objective evolutionary programming (MOEP) using mutation based on adaptive mutation operator (AMO) applied for optimal reactive power dispatch,” ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 14, Jul 2016.

Rebecca Ng Shin Mei, Mohd Herwan Sulaiman, Zuriani Mustaffa, “Ant lion optimizer for optimal reactive power dispatch solution,” Journal of Electrical Systems, Special Issue AMPE2015, pp. 68-74, 2016.

Mahaletchumi Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman, Mahfuzah Mustafa, Rosdiyana Samad, “Benchmark studies on optimal reactive power dispatch (ORPD) based multi-objective evolutionary programming (MOEP) using mutation based on adaptive mutation adapter (AMO) and polynomial mutation operator (PMO),” Journal of Electrical Systems, vol. 12, no.1, pp. 121-132, 2016.

Rebecca Ng Shin Mei, Mohd Herwan Sulaiman, Zuriani Mustaffa, Hamdan Daniyal, “Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique,” Applied Soft Computing, vol. 59, Pages 210-222, Oct 2017.

X.S. Yang., “Bat algorithm for multi-objective optimisation,” International Journal of Bio-Inspired Computation, vol. 3, no. 5, pp. 267-274, 2011.

A. H. Gandomi, G. J. Yun, X.-S. Yang, and S. Talatahari, “Chaos-enhanced accelerated particle swarm optimization,” Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 2, pp. 327-340, 2012.

O. Abdel-Raouf, I. El-henawy and M. Abdel-Baset, “Chaotic harmony search algorithm with different chaotic maps for solving assignment problems,” International Journal of Computational Engineering & Management, vol. 17, pp. 10-15 ,2014.

Chandragupta Mauryan Kuppamuthu Sivalingam1, Subramanian Ramachandran, Purrnimaa Shiva Sakthi Rajamani, “Reactive power optimization in a power system network through metaheuristic algorithms,” Turkish Journal of Electrical Engineering & Computer Science, vol. 25, no. 6, pp. 4615-4623, 2017.

Power Systems Test Case Archive, [Online] Available:, http://www2.ee.washington.edu/research/pstca/ S.S. Reddy, et al., “Faster evolutionary algorithm based optimal power flow using incremental variables,” Electrical Power and Energy Systems, vol. 54, pp. 198-210, 2014.

S. Surender Reddy, “Optimal reactive power scheduling using cuckoo search algorithm,” International Journal of Electrical and Computer Engineering, vol. 7, no. 5, pp. 2349-2356, 2017.

Chaotic based Pteropus algorithm for solving optimal reactive power problem (Lenin Kanagasabai)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 270~275 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp270-275



270

Implementation of anti-collision train prototype based on arduino microcontroller Ahmed R. Ibrahim, Ziad M. Abood College of science, Al-Mustansiriyah University, Iraq

Article Info

ABSTRACT

Article history:

Because the electrical train become a popular mode and Eco-Friendly of transport in largest cities in the world in our day life and due to increase in the train accidents. In this paper a train collision avoidance system is designed and implemented using Arduino NANO and an ultrasonic sensor.

A prototype is used to explain the system function using; two train samples on single track. The ultrasonic sensor is connected to the Arduino NANO to transmit the measured distance to the microcontroller and make the decision to stop the train. The ultrasonic sensor on both trains from the front end and continually measures the distance between them and when they reach the decided distance a signal to the trains engines will slow down or stop both trains to avoid a collision while when the ultrasonic sensors from the rear-end continually measure the distance between them and when it reaches the decided distance a signal to the train engine will slow down and stop the first train to avoid a collision.

Received Apr 15, 2020 Revised May 20, 2020 Accepted Jun 15, 2020 Keywords: Anti-collision Arduino NANO Electrical train Ultrasonic sensor

This is an open access article under the CC BY-SA license.

Corresponding Author: Ahmed R. Ibrahim, College of science, Al-Mustansiriyah University,

Baghdad, Iraq.

Email: ahmed.rasheed86@yahoo.com

1.

INTRODUCTION Decision making systems are widely used automatically at places that human can’t deal with it alone, or places that necessarily more care, safety and accident avoidance. Smart automatic decision-making systems are classified as systems or device that human independent systems which doesn't require any enter for human, which are programmed and designed to operate independently. Anti-collision systems are smart systems that are used within vehicles, trains and airplanes to avoid accident problems. These systems must be potent and dependable in order to avoid mistakes and catastrophes [1].

In general, traffic administration and tracking decrease the amount of accidents that happen in roads or on railways. Especially, train collision avoidance is to be considered in this project. The railway is a popular mode and Eco-Friendly of Transport in largest cities in the world. One of the most comfortable and widely used of the transportation system is train. As the railway network is considered to be the safest and easiest network. More than 10 billion of people and 1050 millions of cargo travel by train per annum.

Transport by Railway irreplaceable in our daily modern life, both for business and private users [2].

Today, rail networks across the world become deals with trains that travelling faster and carrying more passengers and heavier loads than ever before. For these factors the existing infrastructure has put under considerable pressure, which leading to increased request in the maintenance and check of rail assets.

But nowadays, it is not that much safer as the lot of accidents occur due to improper communication between

Journal homepage: http://ijaas.iaescore.com

Int J Adv Appl Sci

ISSN: 2252-8814



271

the network, wrong signaling, worst weather condition or immediate route change. The proper information doesn't get to the train driver on time and before time, so the probability of accidents occur is high [3].

Due to increase in the used of the electric trains in the world and the high speed with the low fares more people prefer to use train but with many risks in the train accidents. In this paper a prototype will be designed and implemented for train collision avoidance system using Arduino NANO and an ultrasonic sensor to reduce the occurrence of accidents between trains on the railways due to many reasons that may be technical or human made errors.

2.

TRAIN COLLISIONS Collisions are the worst accidents. Due to speed moving of trains it will be very difficult to stop such a collision, which need a lead distance to stop. Collisions are happening due to human errors and/or faulty equipment. There are two types of collisions are available. A Head-On collision is one where the front ends of two ships, trains, planes or vehicles hit each other [4]. Figure 1 illustrates the Head-On Collision and A Rear-End collision often implies a collision on a single line railway as illustrated in Figure 2.

Figure 1. Head-on collision

Figure 2. Rear-end collision

3.

ANTI-COLLISION TRAIN PROTOTYPE SYSTEM PARTS In this section of the paper, the used hardware to implement the proposed system, such as the Arduino microcontroller, motor driver unit, and the ultrasound sensor, will be described.

3.1. Arduino microcontroller Arduino Nano is an integrated microcontroller board based on the SMD ATmega328p microcontroller. An FTDI FT232RL on the board channels this are a serial communication over USB and the FTDI drivers (included with the Arduino software) provide a virtual COM-port to the software on the computer [5].

The board can operate via an external supply from 7- to 20 volts. When the supplied voltage less than 7V, the 5V pin may supply less than 5volts and the board may be unstable in performance. If using more than 12V, the voltage regulator may be overheating and damage the board, the recommended range is 7 to 12 volts [6-8].

Implementation of anti-collision train prototype based on arduino microcontroller (Ahmed R. Ibrahim)

272



ISSN: 2252-8814

Table 1 shown below illustrate the specifications of ATmega-328 microcontroller that used in the architecture of the Arduino NANO and Figure 3 illustrates the Arduino NANO microcontroller [9].

Table 1. Specifications of ATmega-328 microcontroller Microcontroller Rated operating voltage Recommended input voltage Input voltage limits I/O digital pins Analogue Input pins DC current per the I/O pin DC current of 3.3v pin Flash memory SRAM EEPROM Clock speed

ATmega 328P 5v 7V to 12V 6V to 20V 14 8 40 mA 50 mA 32 KB of which 0.5KB used for boot loader 2 KB 1 KB 16 MHZ

Figure 3. Arduino UNO microcontroller

3.2. Ultrasonic sensor The Ultrasonic convert ultrasound waves into electrical signals or vice versa, this type of sensors is consisting of a transmitter and a receiver enabling to determine the distance from an object without losing in the accuracy, except in presence of objects made of sound-absorbing materials which obviously make the measurements inaccurate. The Timing diagram is shown in the Figure 4, to start the operation of ultrasonic only need to supply a short 10𝜇𝑠 pulse to the trigger input to start the ranging, and then the module will send out an 8-cycle burst of ultrasound with 40 kHz and wait the reflected signal as an echo that can hit objects within range from (2cm to 400cm) [10, 11]. The speed of signal in free space approximately within 345m/s and according to newton's law can determine the distance ultrasonic and colliding object as the (1). Figure 5 illustrates the ultra-sonic HC-SR05 module. d=N×t, where: d= Distance between ultrasonic and colliding object, N=Speed of travelling signal. t=Time of echo signal.

Figure 4. Timing diagram to start the operation of the ultrasonic module Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 270 – 275

(1)

Int J Adv Appl Sci

ISSN: 2252-8814



273

Figure 5. Ultrasonic HC-SR05 module

3.3. L293D motor driver The most common method of a DC motor speed control through pulse width modulation technique, the motor speed mutation is accomplished by decreasing or increasing the time interval of the signal, the signal has the logic value "1" (TON) and logic "0" value (TOFF), the duty cycle is the ratio of T ON over the time periodic, the speed of DC motor directly proportional with applied voltage, at a duty-cycle of 100% this meaning full output voltage and the motor run with full speed. At a duty-cycle of 50 %, the motor will run with half of the rated speed. The figure 6. Illustrate the relation between speed and applied voltage, the duty cycle can be calculated by (2) [12, 13]. 𝐷=

𝑇𝑜𝑛

(2)

𝑇𝑜𝑛 +𝑇𝑜𝑓𝑓

Figure 6. Speed versus applied voltage

The L293D a simple and low cost used to drive a small DC motor, this driver is a typical Motor driver or Motor Driver IC which allows DC motor to drive on either direction or speed control. L293D is a 16-pin IC which can control a set of two DC motors simultaneously in any direction [14, 15]. The Figure 7 shown below illustrate the pin configuration of the L293D motor driver

Figure 7. L293D motor driver

4.

SYSTEM DESIGN AND IMPLEMENTATION This part explains the design and the implementation of train anti-collision system. The anticollision designed system can be used for both (Head-On Collision and Rear-End Collision); Figure 8 shows the block diagram for system designed. The ultrasonic sensor will send the signal and wait for the collision to Implementation of anti-collision train prototype based on arduino microcontroller (Ahmed R. Ibrahim)

274



ISSN: 2252-8814

return echo and send it to the Arduino for the purpose of calculating the distance, which will in turn be directed to the motor driver to reduce the speed or stop.

Figure 8. Block diagram of anti-collision train prototype system

At the head-on anti-collision case the ultrasonic sensor is planted on the front head of the train.

When a running train approaches another train from the front end the ultrasonic sensor on both trains continually measures the distance between them and when they reach the decided (threshold) distance (25 cm in this project) a signal to the train's engines will slow down and stop both trains to avoid a collision.

The Figure 9. Shows two trains on the same track, both are in the Head-On case and are stopped at a distance.

Figure 9. Head-on anti-collision case

At the Rear-End anti-collision case also, the ultrasonic sensor is planted on the head of the train.

When a running train approaches another train from the rear-end the ultrasonic sensors on the first (approaching train) continually measure the distance between them and when it reaches the decided (threshold) distance a signal to the train and with help of motor driver engine will slow down and stop the first train to avoid a collision. The threshold distance that is specified in this project is (25 cm). As shown in the Figure 10.

Figure 10. Rear-end anti-collision case Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 270 – 275

Int J Adv Appl Sci

ISSN: 2252-8814



275

5.

CONCLUSION The system is implemented in the prototype railway networks; train accidents can be prevented.

This collision between trains is calculated and colliding trains were alerted. The proposed system in this paper help to reduce the train collision, many human lives can be saved in this system because if someone is stuck or wants to cross the railway network, the ultrasonic sensor will send a signal Arduino and the Arduino take the proper decision to make the train engine slow down or stop the train. The scenario of accident in Trains due to collision will be controlled with the help of this paper. The HC-SR05 ultra-sonic module sensor was used in the design measuring small range of distance, but can develop this work in practice should using the "LIDAR lite V3" this type that measures a distance of up to 40 m with high accuracy.

REFERENCES [1]

[2] [3]

[4] [5]

[6]

[7] [8]

[9]

[10] [11] [12]

[13] [14]

[15]

T. Saijyothsna,P. Umamaheswari, “Collision avoidance of trains by creating mutual communication using embedded system,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 7, 2014.

M. Ganapathi, G. Priyanka, “ Smart system for train crash avoidance,” International Journal of Innovative Technologies, vol. 04, no. 11, pp. 2041-2042, 2016.

T.Dhanabalu, S. Sugumar, S.Suryaprakash, A.VijayAnand, “Sensor based identification system for train collision avoidance,” IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15.

K. Kathirvel, S.Palaniappan, “Collision avoidance of trains by creating mutual communication using embedded system,” International Journal of Computer Science and Mobile Computing, vol. 4 no. 4, pp. 31-35, 2015.

I. O. Misiruk, O. I. Timoshenko, V. S. Taran, and I. E. Garkusha, “Data acquisition system based on arduino platform for langmuir probe plasma measurements,” 2016 2nd Int. Young Sci. Forum Appl. Phys. Eng. YSF 2016 Forum Proc., pp. 128-131, 2016.

I. A. Taha and H. M. Marhoon, “Implementation of controlled robot for fire detection and extinguish to closed areas based on arduino,” Telkomnika(Telecommunication Comput. Electron. Control., vol. 16, no. 2, pp. 654–664, 2018.

Marhoon, H. M., Mahdi, M. I., Hussein, E. D., & Ibrahim, A. R. “Designing and implementing applications of smart home appliances,” Modern Applied Science, vol. 12, no.12, pp. 8-17, 2018.

OO, Akinwole. “Design, simulation and implementation of an Arduino microcontroller based automatic water level controller with I2C LCD display,” International Journal of Advances in Applied Sciences (IJAAS), vol. 9, no. 2, pp. 77-84, 2020.

H. M. Marhoon and I. A. Taha, “Design and implementation-of-intelligent-circuit-breaker-for-electrical currebt sensing and monitoring,” International Journal Of Core Engineering & Management,” vol. 4, no. 2, pp. 39–49, 2018.

A. Dimitrov and D. Minchev, “Ultrasonic sensor explorer,” 2016 19th Int. Symp. Electr. Appar. Technol. SIELA 2016, 2016.

L. Petru and G. Mazen, “PWM control of a DC motor used to drive a conveyor belt,” Procedia Eng., vol. 100, no.

January, pp. 299–304, 2015.

A. Y. Yousef and M. H. Mostafa, “Bidirectional speed control of DC motor based on pulse width modulation using microcontroller,” International Journal of Scientific Research in Science and Technology, vol. 1, no. 5, pp. 52–60, 2015.

Ahmed, Shakir M., Hamzah M. Marhoon, and Osman Nuri, “Implementation of smart anti-theft car security system based on GSM,”International Journal of Engineering & Technology, vol. 7, no. 4, pp. 5261-5265, 2018.

Mohammed, Omar Khalaf, Oguz Bayat, and Hamzah M. Marhoon, “Design and implementation of integrated security and safety system based on internet of things,” International Journal of Engineering & Technology, vol. 7, no. 4, pp. 5705-5711, 2018.

Al-Kadhimi, Aymen M., Hamzah M. Marhoon, and Zeyad A. Karam, “Implementation of cell phone detection mobile robot for restricted areas using NodeMCU,” Iraqi Journal of Information & Communications Technology, vol. 1, no. 1, pp. 27-35, 2018.

Implementation of anti-collision train prototype based on arduino microcontroller (Ahmed R. Ibrahim)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 276~283 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp276-283



276

Design of frequency selective surface comprising of dipoles using artificial neural network Monojit Rudra1, P Soni Reddy2, Rajatsubhra Chakraborty3, Partha Pratim Sarkar4 1,2,4Department 3Department

of Engineering & Technological Studies, University of Kalyani, India of Computer Science and Engineering, Future Institute of Engineering and Management, India

Article Info

ABSTRACT

Article history:

This paper depicts the design of Frequency Selective Surface (FSS) comprising of dipoles using Artificial Neural Network (ANN). It has been observed that with the change of the dimensions and periodicity of FSS, the resonating frequency of the FSS changes. This change in resonating frequency has been studied and investigated using simulation software.

The simulated data were used to train the proposed ANN models. The trained ANN models are found to predict the FSS characteristics precisely with negligible error. Compared to traditional EM simulation softwares (like ANSOFT Designer), the proposed technique using ANN models is found to significantly reduce the FSS design complexity and computational time.

The FSS simulations were made using ANSOFT Designer v2 software and the neural network was designed using MATLAB software.

Received Apr 16, 2020 Revised Jun 14, 2020 Accepted Jun 18 2020 Keywords: ANN FSS MLP

This is an open access article under the CC BY-SA license.

Corresponding Author: Partha Pratim Sarkar, Department of Engineering & Technological studies, University of Kalyani, Nadia, West Bengal, 741235, India.

Email: parthabe91@yahoo.co.in

1.

INTRODUCTION In microwave engineering, Frequency Selective Surfaces (FSSs) are planar periodic arrays of metal patches on a substrate or slots on a conducting sheet that function as a filter for free space radiation [1]. Many authors have made analysis on FSS through EM numerical methods, such as the Method of Moment [2]. But these numerical methods require high computational cost. So to avoid this, Artificial Neural Network (ANN) which are previously trained with results obtained by Method of Moment can be used for analysis of FSS [3-4]. Also, other algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) can be blended with ANN for faster and accurate training of the ANN [5–16].

In this paper, patch type FSS consisting of dipoles (as shown in Figure 1) is used whose dimensions (i.e. patch length ‘L’, patch width ‘W’, x-periodicity ‘Tx’ and y-periodicity ‘Ty’) are varied and the corresponding resonating frequencies are noted. Then using these simulated results, some neural network models are designed and trained, which can be used for faster analysis and design of FSS comprising of dipoles.

Journal homepage: http://ijaas.iaescore.com

Int J Adv Appl Sci

ISSN: 2252-8814



277

Figure 1. FSS comprising of dipoles: (a) geometry of unit cell, (b) geometry of array

2.

BACKPROPAGATION TRAINING ALGORITHM The Backpropagation is an algorithm for supervised training of ANN in which the error is propagated backward for update of weights of different layers of the neural network. A Multi-Layer Perceptron (MLP) is shown in Figure 2, having one input layer, one hidden layer and one output layer.

Figure 2. Example of an ANN model

The steps for Backpropagation Algorithm are as follows: 1. Weights and learning rate (α) are initialized Hidden Layer Weights: w1, w2, w3, w4 Output Layer Weights: w5, w6, w7, w8 Bias Weights: a1, a2, b1, b2 Learning Rate (α): 0.0001 2. Steps 3 to 10 are performed when stopping condition is false.

3. Steps 4 to 9 are performed for each training pair.

4. Each input unit receives input signal (ii) and sends it to the hidden unit.

5. Each hidden unit sums its weighted input signals to calculate the net input (net h i). net h1 = w1 i1 + w3 i2 + a1 net h2 = w2 i1 + w4 i2 + b1 Then an Activation function is applied to the net input to calculate the output of the hidden unit (hi).

The output of the hidden layer is then sent to the output layer units. hi = f(net hi) Here Bipolar Sigmoid Activation Function is used: f(x) = 1 / (1 + e-x) 6. Similarly, for each output unit the net input (net o i) is calculated and the activation function is applied to compute the output signals (oi).

Design of frequency selective surface comprising of dipoles using artificial neural network (Monojit Rudra)

278



ISSN: 2252-8814

7.

Each output unit receives a target pattern (ti) corresponding to the input training pattern (ii) and computes the error correction term(𝛿i): 𝛿1 = (t1 - o1) f '(net o1) 𝛿2 = (t2 - o2) f '(net o2) Where, f '(x) is the derivative of f(x) These error correction terms are used to calculate the change in weights (Δwi) and change in bias weights (Δai and Δbi). Δw5 = α𝛿1h1 Δa2 = α𝛿1 SimilarlyΔw6, Δw7, Δw8 and Δb2 are calculated 8. Similarly each hidden unit calculates its error correction term(𝛿i j): 𝛿56 = 𝛿1 w5 f '(net h1) + 𝛿2 w6 f '(net h1) 𝛿78 = 𝛿1 w7 f '(net h2) + 𝛿2 w8 f '(net h2) These error correction terms are used to calculate the change in weights and bias weights. Δw1 = α𝛿56 i1 Δa1 = α𝛿56 SimilarlyΔw2, Δw3, Δw4 and Δb1 are calculated 9. Each output unit and each hidden unit updates its bias weights and weights. wi(new)=wi(old) +Δwi ai (new)=ai (old) +Δai bi (new)=bi (old) +Δbi 10. The stopping condition is checked. The stopping condition may be certain number of epochs reached or when there is previously settled minimum error between actual output and target output.

3.

RESEARCH METHOD Here 5 MLPs are used. Each of them have 4 inputs and 1 output as shown in Figure 3, but the inputs and outputs are different as shown in Table 1.

Figure 3. Common ANN model for all 5 networks

Table 1. Inputs and outputs of the 5 MLPs Neural Networks Network 1 Network 2 Network 3 Network 4

Network 5

Inputs

Output

In Network 1, the 4 inputs are the length, width, xperiodicity and y-periodicity of the proposed FSS In Network 2, the 4 inputs are the length, width, xperiodicity and resonant frequency of the proposed FSS In Network 3, the 4 inputs are the length, width, resonant frequency and y-periodicity of the proposed FSS In Network 4, the 4 inputs are the length, resonant frequency, x-periodicity and y-periodicity of the proposed FSS In Network 5, the 4 inputs are the resonant frequency, width, x-periodicity and y-periodicity of the proposed FSS

In Network 1. The output is the resonant frequency of the proposed FSS In Network 2. The output is the y-periodicity of the proposed FSS In Network 3. The output is the x-periodicity of the proposed FSS In Network 4. The output is the width of the proposed FSS

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 276 – 283

In Network 5. The output is the length of the proposed FSS

Int J Adv Appl Sci



ISSN: 2252-8814

279

4.

RESULTS AND ANALYSIS Initially, the proposed ANN models are trained using the simulation results obtained from ANSOFT Designer v2 software. The training data set is provided in Table 2.

Table 2. Training data set for ANN models Data Set1 Data Set2 Data Set 3 Data Set4 Data Set5 Data Set6 Data Set7 Data Set8 Data Set9 Data Set10 Data Set11 Data Set12 Data Set13 Data Set14 Data Set15 Data Set16 Data Set17 Data Set18

Patch Length 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 9 mm 12 mm 15 mm 6 mm–20 mm 6 mm–20 mm 6 mm–20 mm 7 mm–20 mm 7 mm–20 mm

Patch Width 1.5 mm 1.5 mm 1.5 mm 1.5 mm 1.5 mm 1.5 mm 1.5 mm 1.5 mm 1.5 mm 1.5 mm 1.2 mm–2 mm 0.5 mm–2 mm 0.1 mm–1.5 mm 0.7 mm 0.9 mm 1.1 mm 1.3 mm 1.5 mm

x-periodicity 16.5 mm 18 mm 19.5 mm 21 mm 22.5 mm 15.5 mm–22.5 mm 15.5 mm–22.5 mm 15.5 mm–22.5 mm 15.5 mm–22.5 mm 15.5 mm–22.5 mm 22.5 mm 22.5 mm 22.5 mm 22.5 mm 22.5 mm 22.5 mm 22.5 mm 22.5 mm

y-periodicity 2.4 mm–15 mm 2.4 mm–15 mm 1.5 mm–15 mm 2.4 mm–15 mm 1.95 mm–15 mm 4.2 mm 6.9 mm 9.6 mm 12.3 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm 15 mm

Step Size 0.9 mm 0.9 mm 0.9 mm 0.9 mm 0.45 mm 0.5 mm 0.5 mm 0.5 mm 0.5 mm 0.5 mm 0.3 mm 0.1 mm 0.1 mm 1 mm 1 mm 1 mm 1 mm 1 mm

Next, the performance of the proposed ANN models in accurately predicting the FSS characteristics is validated using a test data. Here, an FSS comprising of dipoles having dimensionsL = 15 mm, W = 1.5 mm, Tx = 22 mm, Ty = 8 mm (as shown in Figure. 1) is used as a test data for the proposed ANN model validation. The dimensions considered here for testing the ANN models is not included in the data set used for training the MLPs. The FSS is simulated using ANSOFT Designer v2 software and its resonant frequency obtained is 8.15 GHz. Next, this data is used for checking the error of all the 5 MLPs, as shown in Table 3.

Finally, the 1st MLP (having L, W, Tx and Ty as inputs and resonating frequency (RF) as output) is used to perform parametric study as given in Section 4.2.

4.1. Single value check (interpolation) of the proposed ANN The validation result of the 5 MLPs using the single test data is given in Table 3. MLP1 is found to predict the resonating frequency (RF) of the FSS for the given set of FSS design parameters with 0.076% error. MLP 2–5 predicts the FSS design parameter (y-periodicity, x-periodicity, Patch Width, and Patch Length, respectively) with 0.94%, 1.724%, 0.0298%, and 0.056% error, respectively for the given input set as shown in Table 3.

Table3. Result for single value check (interpolation) ANN Network MLP 1 MLP 2 MLP 3 MLP 4 MLP 5

Input 1

Input 2

Input 3

Input 4

Patch Length 15 mm Patch Length 15 mm Patch Length 15 mm Patch Length 15 mm Patch Width 1.5 mm

Patch Width 1.5 mm Patch Width 1.5 mm Patch Width 1.5 mm x-periodicity 22 mm x-periodicity 22 mm

x-periodicity 22 mm x-periodicity 22 mm y-periodicity 8 mm y-periodicity 8 mm y-periodicity 8 mm

y-periodicity 8 mm RF 8.15 GHz RF 8.15 GHz RF 8.15 GHz RF 8.15 GHz

Simulated Output RF 8.15 GHz y-periodicity 8 mm x-periodicity 22 mm Patch Width 1.5 mm Patch Length 15 mm

ANN Output

% Error

RF 8.1562 GHz y-periodicity 8.0754 mm x-periodicity 21.6207 mm Patch Width 1.4996 mm Patch Length 15.0084 mm

0.076 % 0.94 % 1.724 % 0.0298 % 0.056 %

An FSS is fabricated using parameters corresponding to MLP1 given in Table 3. The fabricated prototype is shown in Figure 4(a). The transmission characteristic of the fabricated FSS is measured using R&S VNA (ZNB20) and Tx/Rx horn antennas. The simulated and measured transmission (S21) characteristic of the fabricated FSS is given in Figure 4(b). It is found that the resonant frequency of the proposed FSS is nearly same in both simulation and measurement.

Design of frequency selective surface comprising of dipoles using artificial neural network (Monojit Rudra)

280



ISSN: 2252-8814

(a)

(b)

Figure 4. (a) Fabricated prototype of the proposed FSS, (b) Simulated and measured transmission characteristics of the proposed FSS 4.2. Parametric study In this section, extensive study has been conducted to test the performance of MLP 1. MLP 1 can only provide the resonating frequency (RF) as the output for the change in FSS design parameters- Patch Length, Patch Width, x-periodicity and y-periodicity. The values of FSS design parameters for this parametric study are tabulated in Table 4. Here, comparison is done between the simulated result obtained from Ansoft Designer and ANN.

Table 4. Data for parametric study Parametric Study 1 Parametric Study 2 Parametric Study 3 Parametric Study 4

Constant Parameters Patch Width = 1.5 mm, x-periodicity = 22.5 mm, yperiodicity = 15 mm Patch Length = 15 mm, x-periodicity = 22.5 mm, yperiodicity = 15 mm Patch Length = 15 mm, Patch Width = 1.5 mm, yperiodicity = 12.3 mm Patch Length = 15 mm, Patch Width = 1.5 mm, xperiodicity = 22.5 mm

Variable Parameters Patch Length = 7 mm to 20 mm with step 1 mm Patch Width = 0.1 mm to 1.5 mm with step 0.1 mm x-periodicity = 15.5 mm to 22.5 mm with step 0.5 mm y-periodicity = 1.95 mm to 15 mm with step 0.45 mm

Figure 5 shows the results of Parametric Study 1 where Patch Width, x-periodicity, y-periodicityare kept constant and Patch Length is varied. Figure 6 shows the results of Parametric Study 2 where Patch Length, x-periodicity, y-periodicity are kept constant and Patch Width is varied. In Figure 7 the results of Parametric Study 3 is given where Patch Length, Patch Width, y-periodicity are kept constant and xperiodicity is varied. Figure 8 shows the results of Parametric Study 4 where Patch Length, Patch Width, xperiodicity are kept constant and y-periodicity is varied.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 276 – 283

Int J Adv Appl Sci

ISSN: 2252-8814



281

Figure 5. Results of parametric study 1: Variation of patch length from 7 mm to 20 mm with step 1 mm

Figure 6. Results of parametric study 2: Variation of patch width from 0.1 mm to 1.5 mm with step 0.1 mm

Figure 7. Results of parametric study 3: variation of X-periodicity from 15.5 mm to 22.5 mm with step 0.5 mm

Figure 8. Results of parametric study 4: variation of Y-periodicity from 1.95 mm to 15 mm with step 0.45 mm

4.3. Performance comparison of the proposed technique using ANN with traditional EM simulation software (like Ansoft Designer) In traditional EM simulation software, analysis on FSS is done using EM numerical methods, such as the Method of Moment (MOM). These numerical methods require high computational cost, so even with a system with high configuration, the simulation time remains very long. But if instead ANN is used, then it doesn’t require any complex numerical methods, so naturally the computation time is reduced as compared to traditional EM simulation software. Here, firstly an FSS with patch-length = 15 mm, patch-width = 1.5 mm, Design of frequency selective surface comprising of dipoles using artificial neural network (Monojit Rudra)

282



ISSN: 2252-8814

x-periodicity = 22.5 mm and y-periodicity = 15 mm is taken. |S21| vs. Frequency plot is done from 2 GHz to 12 GHz with low step count and for this the time elapsed is 6 minutes. From this plot the approximate resonant frequency is located somewhere between 6 GHz and 7.5 GHz. Then again plot is done from 6 GHz to 7.5 GHz with high step count which took 2 minutes time and the resonant frequency obtained is 6.83 GHz. So, the total simulation time required is 8 minutes.

Then an FSS with patch-length = 8 mm, patch-width = 0.9 mm, x-periodicity = 22.5 mm and yperiodicity = 15 mm is taken. |S21| vs. Frequency plot is done from 2 GHz to 12 GHz with low step count and for this the time elapsed is 4 minutes. From this plot the approximate resonant frequency is located somewhere between 10 GHz and 11.5 GHz. Then again plot is done from 10 GHz to 11.5 GHz with high step count which took 2 minutes time and the resonant frequency obtained is 10.76 GHz. So, total simulation time required is 6 minutes. But in case of ANN, generation of |S21| vs. Frequency plot is not required, as it directly gives resonant frequency as output. In first case the resonant frequency obtained is 6.8348 GHz, and in the second case the resonant frequency obtained is 10.7404 GHz. Here in both the cases the ANN gave resonant frequency output in less than 1 sec time (i.e. in the first case 0.36036 sec and in the second case 0.27701 sec). The percentage reduction in computational time is 99.9249 % in the first case and 99.9230 % in the second case, as shown in Table 5.

Table 5. Performance comparison of the proposed technique using ANN with traditional EM simulation software Parameters

Simulation Time

ANN Output Time

% Reduction

patch-length = 15 mm patch-width = 1.5 mm x-periodicity = 22.5 mm y-periodicity = 15 mm patch-length = 8 mm patch-width = 0.9 mm x-periodicity = 22.5 mm y-periodicity = 15 mm

8 minutes

0.36036 sec

99.9249 %

6 minutes

0.27701 sec

99.9230 %

5.

CONCLUSION In this paper, an Artificial Neural Network model is trained using data obtained from ANSOFT Designer v2 software which uses Method of Moment for analysis of FSS. Hence, the ANN is properly trained and gives negligible error. Using this ANN, the results are obtained very quickly which saves time and also reduces the computational cost and complexity.

REFERENCES [1] [2]

[3]

[4]

[5]

[6]

[7] [8] [9]

Ben A. Munk, “Frequency Selective Surfaces – Theory and Design,” Wiley, no. 1, pp. 1-5, 2000.

Jianxun Su and Xiaowen Xu, “MOM analysis of the planar and curved FSS based on dipole elements,” International Conference on Microwave Technology and Computational Electromagnetics (ICMTCE 2009), Beijing, China, pp. 127-130, 2009.

P.L. da Silva and A.G. D’Assuncao, “Neuromodelling of Frequency Selective Surfaces and E-Shaped Microstrip Antennas,” The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada, pp. 2374-2377, 2006.

P.H.F. Silva and A.L.P.S. Campos, “Fast and accurate modelling of frequency selective surfaces using a new modular neural network configuration of multilayer perceptrons,” IET Microwaves, Antennas & Propagation, vol. 2, no. 5, pp. 503-511, 2008.

P.H. da F. Silva, P. Lacouth, G. Fontgalland, A.L.P.S. Campos and A.G. D’Assuncao, “Design of frequency selective surfaces using a novel MoM-ANN-GA technique,” 2007 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference, Brazil, pp. 275-279, 2007.

R.M.S. Cruz, P.H. da F. Silva and A.G. D’Assuncao, “Synthesis of crossed dipole frequency selective surfaces using genetic algorithms and artificial neural networks,” 2009 International Joint Conference on Neural Networks, Atlanta, GA, USA, pp. 627-633, 2009.

P.H. da F. Silva, R.M.S. Cruz and A.G. D’Assunção, “Blending PSO and ANN for Optimal Design of FSS Filters With Koch Island Patch Elements,” IEEE Transactions on Magnetics, vol. 46, no. 8, pp. 3010-3013, 2010.

Anuradha, A. Patnaik, S.N Sinha and J.R. Mosig, “Design of customized fractal FSS,” Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation, Chicago, IL, USA, 2012.

M.R. da Silva, C. de L. Nóbrega, P.H. da F. Silva and A.G. D’Assunção, “Optimal design of frequency selective surfaces with fractal motifs,” IET Microwaves, Antennas & Propagation, vol. 8, no 9, pp. 627-631, 2014.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 276 – 283

Int J Adv Appl Sci

ISSN: 2252-8814



283

[10] W. C. Araujo, A. G. D’Assunção Junior, E. E. C. Oliveira and A. G. D’Assunção, “FSS designs using a populationbased hybrid algorithm inspired on the echolocation of bats,” 2015 9th European Conference on Antennas and Propagation (EuCAP), Lisbon, Portugal, 2015.

[11] M. C. Alcântara Neto, F. J. B. Barros, J. P. L. Araújo, H. S. Gomes, G. P. S. Cavalcante and A. G. D’Assunção, “A metaheuristic hybrid optimization technique for designing broadband FSS,” 2015 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC), Porto de Galinhas, Brazil, 2015.

[12] M. Panda and P. P. Sarkar, “Prediction of Periodicity of FSS Structure Using Particle Swarm Optimization,” I-manager’s Journal on Electronics Engineering, vol. 7, no. 3, pp. 25-31, 2017.

[13] N. Liu, X. Sheng, C. Zhang, J. Fan and D. Guo, “Design of FSS radome using binary particle swarm algorithm combined with pixel-overlap technique,” Journal of Electromagnetic Waves and Applications, vol. 31, no. 5, pp. 522-531, 2017.

[14] M. Panda and P. P. Sarkar, “Hpnna Based Fss Designing: A Case Study,” International Journal of Computer Sciences and Engineering, vol. 6, no. 5, pp. 792-796, 2018.

[15] S. Agharezaei and M. Falamarzi, “Particle Swarm Optimization Algorithm for the Prepack Optimization Problem,” Economic Computation and Economic Cybernetics Studies and Research, vol. 53, no. 2, pp. 289-307, 2019.

[16] M. C. A. Neto, J. Araújo, R. J. S. Mota, F. J. B. Barros, F. H. C. S. Ferreira, G. P. dos S. Cavalcante and B. S. L. Castro, “Design and Synthesis of an Ultra Wide Band FSS for mm-Wave Application via General Regression Neural Network and Multiobjective Bat Algorithm,” Journal of Microwaves, Optoelectronics and Electromagnetic Applications, vol. 18, no. 4, pp. 530-544, 2019.

BIOGRAPHIES OF AUTHORS Monojit Rudra obtained his B. Tech degree in Electronics and Communication Engineering from Future Institute of Engineering and Management, Kolkata in 2016. He is presently pursuing M. Tech in Communication Engineering at the Dept. of Engineering & Technological Studies in University of Kalyani, Nadia. His areas of interest are Frequency Selective Surfaces, Artificial Neural Network.

P Soni Reddy has received the B. Tech. degree in Electronics & Communication Engineering from Academy of Technology, West Bengal, India, in 2011 under the affiliation of former West Bengal University of Technology and the M. Tech. degree in Electronics & Communication Engineering from Kalyani Govt. Engineering College, Kalyani, West Bengal, India, in 2016 under the affiliation of Maulana Abul Kalam Azad University of Technology, West Bengal. She worked as a Guest Lecturer (November 2017 till August 2018) in the Department of Engineering & Technological Studies, University of Kalyani, West Bengal, India. Since April 2019, she has been working as a CSIR Senior Research Fellow (under Grant 09/106(0182)/2019- EMR-I) in the Department of Engineering & Technological Studies, University of Kalyani, West Bengal, India. Her research areas include enhancement of radiation characteristics of microstrip patch and dielectric resonator antennas, and compact multiband DGS integrated microstrip antenna.

Rajatsubhra Chakraborty is a final year B. Tech student of Computer Science Engineering from Future Institute of Engineering and Management, Kolkata.

Dr. Partha Pratim Sarkar obtained his Ph. D in Engineering from Jadavpur University in the year 2002. He has obtained his M.E. from Jadavpur University in the year 1994. He earned his B.E. degree in Electronics and Telecommunication Engineering from Bengal Engineering College in the year 1991. He is presently working in the rank of Professor in the Department of Engineering and Technological Studies, University of Kalyani, since January 2005. His area of research includes Microstrip Antenna, Frequency Selective Surfaces, and Artificial Neural Network. He has contributed to numerous (more than 245 publications) research articles in various journals and conferences of repute. He is a life fellow of IETE and fellow of IE (India).

Design of frequency selective surface comprising of dipoles using artificial neural network (Monojit Rudra)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 284~293 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp284-293



284

Development of software defect prediction system using artificial neural network Olatunji B. L.1, Olabiyisi S. O.2, Oyeleye C. A.3, Sanusi B. A.4, Olowoye A. O.5, Ofem O. A.6 1,2,4,5Department

of Computer Science, Ladoke Akintola University of Technology, Nigeria of Information Systems, Ladoke Akintola University of Technology, Nigeria 6Department of Computer Science, University of Calabar, Nigeria

3Department

Article Info

ABSTRACT

Article history:

Software testing is an activity to enable a system is bug free during execution process. The software bug prediction is one of the most encouraging exercises of the testing phase of the software improvement life cycle. In any case, in this paper, a framework was created to anticipate the modules that deformity inclined in order to be utilized to all the more likely organize software quality affirmation exertion. Genetic Algorithm was used to extract relevant features from the acquired datasets to eliminate the possibility of overfitting and the relevant features were classified to defective or otherwise modules using the Artificial Neural Network. The system was executed in MATLAB (R2018a) Runtime environment utilizing a statistical toolkit and the performance of the system was assessed dependent on the accuracy, precision, recall, and the f-score to check the effectiveness of the system. In the finish of the led explores, the outcome indicated that ECLIPSE JDT CORE, ECLIPSE PDE UI, EQUINOX FRAMEWORK and LUCENE has the accuracy, precision, recall and the f-score of 86.93, 53.49, 79.31 and 63.89% respectively, 83.28, 31.91, 45.45 and 37.50% respectively, 83.43, 57.69, 45.45 and 50.84% respectively and 91.30, 33.33, 50.00 and 40.00% respectively. This paper presents an improved software predictive system for the software defect detections.

Received Mar 23, 2020 Revised Jun 11, 2020 Accepted Jun 18, 2020 Keywords: Artificial neural network Genetic algorithm Software defect prediction Software metrics

This is an open access article under the CC BY-SA license.

Corresponding Author: Olatunji B. L., Department of Computer Science, Ladoke Akintola University of Technology, Gra oke aafin area Ogbomoso north local government, 210214, Ogbomosho, Nigeria.

Email: olatunji_tunde@yahoo.com

1.

INTRODUCTION A software defect is a fault, blunder, or failure in a software system [1]. It creates either an off base, or unforeseen result, and acts in a unintended way [2]. It is a flaw in the software system that makes it perform out of the blue [3]. A software defect can be referred to as imperfection during the software improvement process that makes the software fail and not meets the ideal desire [4]. The defect prediction in software is the way toward deciding pieces of a software system that may contain bugs [5]. Use of Defect Prediction systems in the early software life-cycle permits the pro to focus their testing labor in a way that the parts identified as mistake inclined are tried inside and out in contrast with different pieces of the software system [6] This prompts the decrease of labor costs during improvement and furthermore loosens up the support effort [7]. Late investigations report that the chance of bug discovery by the software defect prediction systems might be higher than the chance of identification by as of now utilized software audits in mechanical strategies [8]. Thusly, the right prediction of defect-inclined software assists with Journal homepage: http://ijaas.iaescore.com

Int J Adv Appl Sci

ISSN: 2252-8814



285

coordinating test effort, to decrease costs, to improve the software testing process by focusing on defectinclined modules [9], lastly to make the nature of the software better [10].

That is the reason today's software defect prediction is a significant examination subject in the software engineering field [11]. Software defect prediction is a key procedure in software engineering to make the quality and affirmation of software better in less time and at least expense [12]. It is actualized before the testing phase of the software advancement life cycle. Software defect prediction systems give defects or various defects.

The software defect prediction has been roused by various analysts to give a different system inside a task or cross-undertaking to improve different quality and watching affirmation of software [12]. There are two ways to deal with builds a software defect prediction system like supervised learning and unsupervised learning. Supervised learning has an issue of requiring historical information to prepare the software defect prediction system while unsupervised learning doesn't require historical information or some known outcomes [2]. The improvement of software technology causes an expansion in the number of software items, and their support has become a difficult assignment. Besides, half of the life cycle cost for a software system incorporates upkeep exercises. With the ascent in complexity in software systems, the likelihood of having defective modules in the software systems is getting higher [13]. A key focus, defect prediction, has risen as a functioning examination zone for decades. Defect prediction methods build systems dependent on different sorts of metrics and foresee defects at different granularity levels, e.g., change, file, or module levels [14]. These procedures can be utilized to effectively apportion quality confirmation assets. In spite of various defects, prediction contemplates research on defect prediction despite everything increments exponentially.

Tending to this issue can give knowledge to the two experts and scientists. Experts can utilize observational proof on defect prediction to settle on informed choices about when to utilize defect prediction and how it would best fit into their advancement procedure. Specialists can improve defect prediction procedures dependent on the desires for professionals and appropriation challenges that they face. To pick up bits of knowledge into the reasonable estimation of defect prediction, a quantitative report was performed in this examination so as to help software designers with the errand of comprehension, assessing, and improving their software items. It is imperative to predict and fix the defects before it is conveyed to clients in light of the fact that the software quality confirmation is a tedious task and now and again doesn't take into consideration complete testing of the whole system because of spending issues. There are numerous open datasets that are accessible free for specialists like PROMISE, ECLIPSE, and APACHE to conquer the difficult issue when preparing performed on another project. Analysts have been creating enthusiasm to build a cross-project defect prediction system with various metrics set like class-level metrics, process metrics, static code metrics yet they couldn't build increasingly feasible systems [12]. There are numerous classifiers or learning algorithm to choose a wide assortment of software metrics like Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Tree, Neural Network and Logistic Regression. Hence, in this paper a software defect prediction system was developed using Artificial Neural Network as the classifying algorithm and with the use of Genetic Algorithm the possibility of overfitting was eliminated by extracting the relevant features from the original datasets which the outcomes give best predictive performance.

2.

RELATED WORK Fenton and Neil [15], make utilization of Bayesian networks for forecasting of unwavering quality and defectiveness of software. It makes utilization of casual process factors and qualitative and quantitative measures, in this manner taking into account the constraints of traditional software impediments.

The utilization of a powerful discretization method brings about a better prediction system for software defects. Jie et al. [16], make utilization of different statistical procedures, and machine learning methods were utilized to verify the validity of software defect prediction systems. In this investigation, the neuro-fuzzy method was thought of. The data from ISBSG were taken to achieve the research. Manu [17], make utilization of another computational insight sequential hybrid design including Genetic Programming (GP) and Group Method of Data Handling (GMDH) viz. The GPGMDH has been contemplated. Be that as it may, the GP and GMDH, a large group of methods on the ISBSG dataset have been tried.

The GP-GMDH and GMDH-GP hybrids surpass all other independent and hybrid procedures. It is presumed that the GPGMDH or GMDH-GP system is the greatest system among all different methods for software cost estimation. Puneet and Pallavi [18] utilized different data mining strategies for software mistake prediction, like affiliation mining, classification, and clustering methods. This has helped the software engineers in growing better systems. For a situation where defect marks are absent, unsupervised procedures can be utilized for system advancement.

Development of software defect prediction system using artificial neural network (Olatunji B. L.)

286



ISSN: 2252-8814

In 2014, Mattias and Alexander worked on software defect prediction utilizing machine learning (Random Forest and J46) on test and source code metrics. The goal of the proposal was to explore whether a test, combined with a source code file contained enough information to upgrade the software defect performance if metrics from both source files and test files are joined. Gray et al. [19] proposed an investigation utilizing the static code metrics for a group of modules contained inside eleven NASA data sets and make utilization of a Support Vector Machine classifier. A careful progression of the pre-processing stage was applied to the data before classification, including the balancing of the two classes (defective or something else) and the dismissal of countless rehashing events. The Support Vector Machine in this trial yields a normal accuracy of 70% on previously inconspicuous data. According to the reviewed related works, it is observed that the previously developed software prediction systems have a limitation of overfitting which happens when the system acquire the detail in the training data to the extent that it negatively effects the performance of the system on new data.

3.

RESEARCH METHOD The architecture of the developed system in this paper is presented in Figure 1. The following are the stages that were adopted in this paper: i.

The first stage is acquisition of data. This stage involves gathering necessary datasets which were used in this paper. However, the datasets were acquired from http://bug.inf.usi.ch/download.php which is publicly available for use. ii. The next stage is the feature selection stage which was achieved by using Genetic Algorithm so as to extract the relevant features from the datasets acquired in the first stage. iii. In the classification stage, the extracted features were classified using Artificial Neural Network. iv. Finally, the results of this work were evaluated using accuracy, precision, recall and f -score.

Figure 1. Architecture of the developed system.

3.1. Data collection Software defect prediction research depends on data that must be gathered from in any case separate stores. In this paper, the datasets were acquired from http://bug.inf.usi.ch/download.php which is a store for the bug prediction dataset for most open-source software. “The Eclipse Jdt Core, Eclipse Pde Ui, Equinox Framework and Lucene” are the software systems that were considered in this paper. However, each software systems includes different pieces of information but in this paper weighted entropy module codenamed “weighted.ent” was selected because it has most familiar parameters like lines of code which suites the aim of defect prediction system. Weighted entropy is the proportion of data provided by a probabilistic test whose basic occasions are described by both their target probabilities and by some subjective loads.

3.2. Feature selection The computational complexity of some of the previously mentioned machine learning algorithms makes the building of the system infeasible to use if all of the features in the dataset is used. Along these lines, feature selection was utilized to remove a lot of most significant free factors contained in the first Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 284 – 293

Int J Adv Appl Sci

ISSN: 2252-8814



287

dataset to dispense with factors that won't add to the presentation of prediction, at that point improve learning proficiency and increment prediction accuracy. However, in this paper Genetic Algorithm (GA) was used for extracting the relevant features in eliminating the possibility of overfitting. GA is a versatile heuristic technique for worldwide advancement looking through used to create valuable answers for machine learning applications and it reenacts the conduct of the development procedure in nature. Figure 2 depicts the flowchart of a typical GA. The feature was ultimately reduced using the fitness function; ∑𝑚 𝑖=1

1 |(∑𝑛 𝑗=1 𝑔(𝑗))−𝑅(𝑖)|

(1)

where 𝑔 is a 𝑚 × 𝑛 matrix of feature and 𝑅 is the corresponding output.

3.3. Classification stage The extracted relevant feature was divided into folds and ensure that each fold was used as testing set at some point and used to train the classifier. K-fold cross validation was adopted where the acquired datasets was divided into a k number of folds. However, since four open source software were considered in this paper the datasets was divided into 4 folds. In the primary cycle, the principal fold was utilized to test the framework and the rest was utilized to prepare the framework. In the subsequent emphasis, the subsequent fold was utilized as the testing set while the rest fill in as the preparation set. This process was repeated until each fold of the 4 folds are been used as the testing set. The system has a flow in which every user can follow. This also can be used in software engineering field when measuring the flow and quality of a software according to software metrics. Cross validation was adopted since the amount of data is limited and it has a merit over the existing technique called holdout method. In the holdout method, one part of the datasets is used for training and the other for testing. In this paper, the solution to the bias idea was adopted using cross validation where all the instances were used one time for testing and training.

This simply means that, instead of conducting four folds, a total of 16 folds is generated and the error estimate is therefore more reliable. Hence, Artificial Neural Network (ANN) was adopted in the classification stage using Levenberg-Marquardt (LM) Algorithm to train the ANN. The choice of the LM Algorithm in this paper is that it is not that memory efficient but faster than other algorithms. It approximates the blunder of the network with a second-order articulation which diverges from the back-propagation algorithm that does it with a first-order articulation. LM refreshes the ANN loads as follows: ∆𝑤 = [𝜇𝐼 + ∑𝑃𝑃=1 𝐽𝑃 (𝑤)𝑇 𝐽𝑃 (𝑤)]−1 ∇𝐸(𝑤)

(2)

where 𝐽𝑃 (𝑤) is the Jacobian matrix of the error vector; 𝑒 𝑃 (𝑤) evaluated in w and 𝐼 is the identity matrix.

The vector error 𝑒 𝑃 (𝑤) is the error of the network for patter 𝑝, that is 𝑒 𝑃 (𝑤) = 𝑡 𝑝 − 𝑜 𝑝 (𝑤)

(3)

The parameter 𝜇 is increased or decreased at each step. If the error is reduced, then 𝜇 is divided by a factor 𝛽 and it is multiplied by 𝛽 in other case. LM performs the steps detailed in Algorithm 1. It calculates the network output, the error vectors and the Jacobian matrix for each pattern. Then, it computes ∆𝑤 using equation 2 and recalculates the error with 𝑤 + ∆𝑤 as network weights. If the error has decreased, 𝜇 is divided by 𝛽, the new weights are maintained and the process starts again; otherwise, 𝜇 is multiplied by 𝛽. ∆𝑤 is calculated with a new value and it iterates again [20].

Development of software defect prediction system using artificial neural network (Olatunji B. L.)

288



ISSN: 2252-8814

Figure 2. The flowchart of a typical genetic algorithm

Algorithm 1: Pseudocode of Levenberg-Marquardt Initialize Weights; While not stop Criterion do Calculates C P (w) for each pattern P

e1 = ∑ = 1 eP (w)T eP (w) P

Calculates J P (w) for each pattern Repeat Calculates ∆w P

e2 = ∑ = eP (w + ∆w)T eP (w + ∆w) P

If e1 ≤ e2 then

μ= μ∗ β End If Until e1 < e2

μ = μ/β w = w + ∆w End while

3.4. Performance metrics In order to measure defect prediction results by classification models, different performance measures are available for effectiveness. In this paper, the following prediction outcomes were considered: Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 284 – 293

Int J Adv Appl Sci

ISSN: 2252-8814



289

i. ii. iii. iv.

True positive (TP): buggy instances predicted as buggy False positive (FP): clean instances predicted as buggy True negative (TN): clean instances predicted as clean False negative (FN): buggy instances predicted as clean With these outcomes, the following measures which are mostly used in the software defect prediction literature are defined: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =

𝑇𝑃+𝑇𝑁 𝑇𝑃+𝐹𝑃+𝑇𝑁+𝐹𝑁

(4)

Accuracy thinks about both true positives and true negatives over all occurrences. As it were, accuracy shows the proportion of all accurately classified cases. 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑅𝑒𝑐𝑎𝑙𝑙 =

𝑇𝑃

(5)

𝑇𝑃+𝐹𝑃

𝑇𝑃

(6)

𝑇𝑃+𝐹𝑁

Recall measures correctly predicted buggy instances among all buggy instances. 𝐹 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒 =

2 ×(𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ×𝑅𝑒𝑐𝑎𝑙𝑙) 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 +𝑅𝑒𝑐𝑎𝑙𝑙

(7)

F-measure is a harmonic mean of precision and recall. By collecting these performance measurements, future predictions on unseen files can be estimated. The calculation of accuracy, precision and recall makes use of the confusion matrix.

4.

RESULTS AND DISCUSSION The experiment was conducted by first extracting the relevant features from the datasets used in this research as discussed in section 3.2 using GA. However, weighted-ent dataset has 17 features excluding the class names and with the adoption of the GA, the features are reduced to 13 using the fitness function discussed in section 3.2. Figure 3 shows the graphical user interface of the GA at the feature selection stage.

Using the mathematical formulas discussed in section 3.4, the values in Table 1 are calculated and by collecting these performance measurements, future predictions on unseen files can be estimated.

According to the conducted experiments the percentage of the True Positive Rate (TPR) and True Negative Rate (TNR) of the datasets used in this research work; ECLIPSE JDT CORE, ECLIPSE PDE UI, EQUINOX FRAMEWORK and LUCENE are (79.31% and 88.24%), (45.45% and 87.97%), (45.45% and 73.81%) and (50.00% and 93.85%) respectively. The training and validation for the datasets ECLIPSE JDT CORE, ECLIPSE PDE UI, EQUINOX FRAMEWORK and LUCENE was conducted. However, the best validation performance is 0.52482 at epoch 5, 0.21032 at epoch 5, 0.67527 at epoch 9 and 0.01356 at epoch 10 respectively. Figures 4, Figure 5, Figure 6 and Figure 7 shows the chart representation of the training and validation for each dataset respectively.

Summarily, K-fold validation method was used to validate the dataset where all the datasets partakes in both training and testing process as discussed in Section 3.3. More so, as shown in Table 1 throughout the performance measures the dataset LUCENE has the highest accuracy of 91.30% while EQUINOX FRAMEWORK has the highest precision of 57.69% which measures how good the prediction system is at identifying actual faulty files. Furthermore, recall used in this research measures the proportion of faulty files which are correctly identified as faulty where ECLIPSE JDT CORE has the highest recall of 79.31% and highest F-Score of 63.89%.

Development of software defect prediction system using artificial neural network (Olatunji B. L.)

290



ISSN: 2252-8814

Figure 3. Desktop interface of the feature selection stage

Table 1. System results of the accuracy, precision, recall, and F-Score Datasets ECLIPSE JDT CORE ECLIPSE PDE UI EQUINOX FRAMEWORK LUCENE AVERAGE

Accuracy 86.93% 83.28% 83.43% 91.30% 86.24%

Precision 53.49% 31.91% 57.69% 33.33% 44.11%

Recall 79.31% 45.45% 45.45% 50.00% 55.05%

F-Score 63.89% 37.50% 50.84% 40.00% 48.06%

Figure 4. Training and validation for ECLIPSE JDT CORE

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 284 – 293

Int J Adv Appl Sci

ISSN: 2252-8814



291

Figure 5. Training and validation for ECLIPSE PDE UI

Figure 6. Training and validation for EQUINOX FRAMEWORK

Figure 7. Training and validation for LUCENE

To ease the comparison to the related study, the average of the results for all the datasets and performance measures are presented in Figure 8. As accuracy is dependent on the balance of the underlying dataset, it is further compared to the average accuracy result of the related study. [21] Proposed Development of software defect prediction system using artificial neural network (Olatunji B. L.)

292



ISSN: 2252-8814

a ConPredictor system to predict defects specific to concurrent programs by combining both static and dynamic program metrics. As this research is conducted using the same performance measures as [21] and as they summarize many studies, the results of this study are compared to ones compiled by [21].

100.00% 80.00% 60.00% 40.00% 20.00% 0.00% Accuracy

Precision

ConPredictor

Recall

F-Score

Developed System

Figure 8. ConPredictor comparison results against the developed system

5.

CONCLUSION The development of software product is increasing exponentially due to their benefits and occurrence of defects in the software products is inevitable. In other words, this defect needs to be reduced to minimum count. Software defect prediction effectively improve the quality and efficiency of software which enhances the procedure of following defective parts in software preceding the beginning of the testing stage.

However, some classification techniques such as Naïve Bayes, random forest and decision tree has been adopted for software defect prediction according to literature. Hence, in this paper GA was successfully used for feature selection alongside ANN in predicting the defective modules in a software system. This developed system was compared with existing system which at the completion of the conducted experiments it outshines the existing system by giving a best predictive performance.

REFERENCES Naik, K. and Tripathy, P., “Software Testing and Quality Assurance,” John Wiley & Sons, Inc. pp. 29-32, 2008.

Sanusi B. A., Olabiyisi S. O., Olowoye A. O. and Olatunji B. L., “Software Defect Prediction System using Machine Learning based Algorithms,” Journal of Advances in Computational Intelligence Theory, vol. 1, no. 3, pp.

1-9, 2019.

[3] McDonald M., Musson, R. and Smith, R., “The practical guide to defect prevention,” Control, pp. 260-272, 2007.

[4] Kumaresh, S. and Baskaran, R., “Defect analysis and prevention for software process quality improvements,” International Journal of Computer Applications, vol. 8, no. 9, pp. 250-254, 2010.

[5] Kitchenham, B. A.,” Guidelines for Performing Systematic Literature Review in Software Engineering,” Technical Report EBSE-2007-001, Keele University and Durham University, Staffordshire, pp. 184-210, 2007.

[6] Catal, C. and Diri, B., “A Systematic Review of Software Fault Prediction studies,” Expert Syst. Appl., vol. 36, pp.

7346–7354, 2009.

[7] Radjenovic, D., Hericko, M., Torkar, R. and Zivkovic, A., “Software fault prediction metrics: A Systematic literature review,” Inf. Softw. Technol, vol. 55, pp. 1397-1418, 2013.

[8] Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y., and Bener, A., “Defect prediction from static code features: current results, limitations, new approaches,” Automated Software Engineering, vol. 17, no. 4, pp. 375407, 2010.

[9] Catal, C., Sevim, U., and Diri, B., “Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm,” Expert Systems with Applications, vol. 38, no. 3, pp. 2347-2353, 2011.

[10] Hall, T., Beecham, S., Bowes, D., Gray, D., and Counsell, S., “A Systematic Literature Review on Fault Prediction Performance in Software Engineering,” IEEE Transactions on Software Engineering, vol. 38, no. 6, pp. 1276-1304, 2012.

[11] Song, Q., Jia, Z., Shepperd, M., Ying, S., and Liu, J., “A General Software Defect-Proneness Prediction Framework,” IEEE Transactions on Software Engineering, vol. 37, no. 3, pp. 356-370, 2011.

[1] [2]

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 284 – 293

Int J Adv Appl Sci

ISSN: 2252-8814



293

[12] Rajesh, K. and Gupta, D. L., “Software Fault Prediction,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 9, pp. 250-254, 2015.

[13] Xu, J. Ho, D. and Carpret, L. F., “An empirical study on the procedure to derive software quality estimation models,” International Journal of Computer Science & Information Technology (IJCSIT), vol. 2, no. 4, pp. 1-16, 2010.

[14] Hata, H., Mizuno, O. and Kikuno, T. “Bug prediction based on fine-grained module histories,” In Proceedings of the 34th International Conference on Software Engineering, ICSE ’12, pp. 200-210, 2012.

[15] Fenton, N. and Neil, M., “Using Bayesian networks to predict software defects and reliability,” Proc. IMechE vol.

222 Part O: J. Risk and Reliability, pp. 702-703, 2008.

[16] Jie Xu, Danny Ho and Luiz Fernando, “An Empirical Study on The Procedure Drive Software Quality Estimation Models,” International journal of computer science & information Technology (IJCSIT), vol. 2, no. 4, pp. 12-15, 2010.

[17] Manu, B., “Computational Hybrids Towards Software Defect Predictions,” International Journal of Scientific Engineering and Technology, vo. 2, no. 5, pp. 311-316, 2013.

[18] Puneet, J. K. and Pallavi, “Data Mining Techniques for Software Defect Prediction,” International Journal of Software and Web Sciences, vol. 3, no. 1, pp. 54-57, 2013.

[19] Gray, D., Bowes, D., Davey, N. and Sun, Y., “Bruce Christianson, Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics,” 11th International Conference, EANN 2019, pp. 21-25, 2019.

[20] Khan, K. and Sahai, A., “Comparison of BA, GA, PSO, BP and LM for Training Feed Forward Neural Networks in E-Learning Context,” Int J Intel Syst App, vol. l7, pp. 23-29, 2012.

[21] Yu, T., Wen, W., Han, X. and Hayes, J., “Conpredictor: Concurrency Defect Prediction in Real-World Applications,” In IEEE International Conference on Software Testing, Verification and Validation, pp. 168-179, 2018.

Development of software defect prediction system using artificial neural network (Olatunji B. L.)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 294~301 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp294-301



294

Application of thermal imaging for detecting cold air leak location in cold storage P. Pathmanaban1, Shanmuga Sundaram Anandan2, B. K. Gnanavel3, C. P. Murigan4 1Department

of Automtobile Engineering, Velammal Engineering College, India of Mechanical Engineering, Sree Sastha Institute of Engineering and Technology, India 3Department of Mechanical Engineering, Saveetha Engineering College, India

2,4Department

Article Info

ABSTRACT

Article history:

Nowadays Cold storage has been playing an important role in preserving the perishable food products like fruits, vegetables, dairy products, fish, and meat etc. The major problem of cold storage is unwanted energy transmission during the operation. It is necessary to maintain the constant temperature during storing the products. There are different kinds of energy loss happening during the operation. One of the major causes is cold air leaking from the inside of the cold room to outside. It is due to poor insulation and improper maintenance of cold storage. It is very difficult to identify the exact location of the leak by the naked eye. In this research work, the cold storage was inspected with the thermal imaging system. Thermal cameras are highly sensitive to temperature and it can detect the variation of temperature ranging from 0.1°C. The measured temperatures are further converted into a colour based pattern. It is known as thermogram. These colour-based thermal patterns are further processed for identifying energy transmission location. It is done by applying various image processing methods such as histogram equalization, diffusion error, otsu thresholding and morphologic function. These techniques were applied to images of cold storages and exact cold air transmission locations were identified.

Received Mar 3, 2020 Revised Apr 24, 2020 Accepted Jul 3, 2020 Keywords: Cold storage Image processing Insulation problem Thermal image

This is an open access article under the CC BY-SA license.

Corresponding Author: P. Pathmanaban, Department of Automtobile Engineering, Velammal Engineering College, Ambattur Red Hills Rd, Velammal Nagar, Surapet, Chennai, Tamil Nadu 600066, India.

Email: pathmanaban@velammal.edu.in

1.

INTRODUCTION Fruits and vegetables start to deteriorate or lose their quality in the following ways: change in weight, texture, flavour, colour, nutritional value and appearance at post-harvesting temperature and tends to reduce the shelf life of the product. Deterioration of the products depends on storage time and temperature [1]. Post-harvest cooling has the potential to reduce the respiratory activity, internal water loss and wilting of the products. It is also helpful to prevent degradation by enzymes, stops the production of a ripening agent, ethylene and the growth of microorganisms causing decay and pathogens. Moreover, postharvest cooling has the tendency to maintain the quality of the products for an extended period and supply it whenever required. It is the important benefit for growers and distributors. Recent day cold storages are highly capable of maintaining organoleptic qualities of the products and reduce spoilage and increase the shelf life [2]. Infiltration of water moist air into cold storage unit cause many problems which includes increased cost of running, defrosting the refrigeration system, mist formation [3]. Moreover, the temperature fluctuation causes weight loss and quality deterioration of the products [4]. Prediction and measurement of Journal homepage: http://ijaas.iaescore.com

Int J Adv Appl Sci

ISSN: 2252-8814



295

the cold air infiltration through doors by an analytical model has been proved in that Gosney model is better for predicting infiltration [5]. Cold storage consumes more energy than common buildings for maintaining lower temperatures. In the food industry, the refrigeration system is the only technology with the high cost and produce more emission, its approximately 1 % of global GXG emissions [6]. It very well may be seen that the energy demand varies significantly from various kinds and areas of cold storages. Meanwhile, cold storages are the structures with huge warm mass, the transient energy utilization of which much depends on the variety of cooling heap of interior mass. To full fill the requirement, it is the necessity of cold storage exceptionally relies on its types and area. Hence, with an enormous energy devouring per storage volume, for the reasons to discover retrofit exponential of existing cold storage and better handling of the energy supply, an examination and quantization of the influencing variables of the transient energy consumption is important for users [7]. The innovation of phase change materials utilized in the cold room has incredible potential in energy storage and cost sparing under the foundation of peak and valley price of electricity [8].

In recent days the thermal imaging system has been used as an inspection tool for measuring different temperatures. It is working with the principle of infrared radiation (IR) to absorb the temperature and the radian energy of the object. Its major advantages are non-contact and not alter the temperature of the measuring objects. Moreover, it has the potential to measure wide temperature ranges, highly sensitive, low power consumption, and long life. In addition to that it is easy to operate, reliable and safe [9]. In Nondestructive testing, TI has been used to detect the material's defects and inhomogeneity by its strange thermal behavior [10]. Identification of a defect in surface and under the surface can evaluate by inspecting the contrast of the thermal image pixels. It is done by using the undamaged area pixel contrast as reference [11]. In civil engineering TI system is used to inspect and ensure the quality of the building by detecting the surface defects in structures [12]. In this research work, cold rooms are inspected with thermal camera and images are processed and analyzed with image processing tool in MAT lab.

2.

MATERIAL AND METHODS In this experimentation, two-ton capacity cold storage was used. The cold storage has been fully loaded with potatoes, which was collected from local agricultural lands in Chennai. Potatoes are stacked in the plastic bins and then it was placed in order. As per the regulatory guidance for storing temperature of potatoes, the room temperature was maintained 10 oC with 90% humidity range [13]. The inside temperature was controlled by a control panel placed outside the cold storage. The environment temperature of Cold storage has also been maintained with a consistent temperature. The output and input parameters of the cold storage were regularly monitored and noted. A detailed description of the cold storage unit is provided in Table 1. To inspect the cold storage, FLIR E60 model camera used. Before taking the images, the outside environment of the cold storage was set as constant temperature. The automatic control system has been used to change ambient temperature and relative humidity of the cold storage. The calculation of the transmission heat created by walls, floor and ceiling requires information on thickness and type of isolation material used in construction of cold room, construction of building, physical specifications of the cold storage volume, inside and outside environment temperatures. Following equations were used to calculate transmission heat [14, 15]. qc = K A (tout −ti) qc = transmission heat at flat surface (kCal) K= Total heat transmission coefficient (kCal/hm2°C) A= Area of heat transmission (m2) to= Temperature of outside or neighbour volume (°C) ti= Inside volume temperature (°C) 1 K

=

1 αi

+ ∑ni=1.

xi li

+

1 αo

(1)

Where: αo= Coefficient of heat transmission of outside surface (kCal/hm 2 °C) αi= Coefficient of heat transmission of inside surface (kCal/hm2 °C) xi = Material thickness (m) li = Thermal conductivity (kCal/hm °C) The proposed method helps evaluate a leak of cold air through the introduction of image processing techniques. The following steps were taken to evaluate the exact location of the leak.

1. Acquisition of the image using a thermal camera.

2. Screening the image using filters.

3. Enhance the image.

Application of thermal imaging for detecting cold air leak location in cold storage (P. Pathmanaban)

296

 4.

ISSN: 2252-8814 Compare the results

Figure 1. Flow chart the system

The image processing techniques currently evaluated are histogram, Otsu thresholding, diffusion error and morphological function (1) show the flow chart of the system. The purpose of this research is to detect a cold air leak location in a cold storage unit.

3. FLOWCHART AND EXPLANATION 3.1. Image acquisition The quality of the thermal images depends on controllable parameters such as ambient temperature, airflow and lighting. It is necessary to maintain the temperature at 30°C, without air draft and diffused lighting. Thermal camera has been used to inspect the cold storage after 24 hrs of installation. The thermal camera switched to video mode and recorded the entire cold storage from the outside. It is observed that the thermal camera shows most of the region with yellow colour and some of the regions with a blue colour.

Based on this variation the blue spot region is marked and images of that region were taken for further analysis. Thermal images were captured at where the low-temperature region is identified. Each image is analyzed and few images are selected for image processing.

3.2. Histogram method Two sets of images are considered for the analysis-thermal cold storage image and trial image for training. The trial thermal pattern was generated using computational fluid dynamics software (Star CCM+).

The trial image is generated such that, the colour blue depicts the lowest temperature and extends to the colour red which stands for the highest temperature [16]. The analysis was performed in the numerically generated image and then repeated for the thermal image of the cold storage Table 1. The cold storage unit Image Name Image1 (No leak) Image2 (Moderate leak) Image3 (Light Leak) Image4 (SevereLeak)

Zeros 52 0 38 0

Non-Zeros 204 218 256 256

RMS Error 0 65.37 54.01 80.54

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 294 – 301

SNR 0.000 -9.94 -7.53 -11.64

Median 206 193 185 190

Stddv 9.7e+002 4.3e+002 5.5e+002 2.5e+002

Int J Adv Appl Sci



ISSN: 2252-8814

297

3.2.1.

RGB to grey conversion RGB is converted into a greyscale image, which is the intensity image. This conversion occurs by eliminating hue and saturation and retaining luminance.

3.2.2.

Noise filtering The median filter is used in this paper for sorting out salt and pepper noise. It keeps the sharp edges and is a non-linear operation. For every input pixel p(x, y), the value of the pixel is found, its median is identified and the value is assigned to output pixel q(x, y). After acquiring the image, it is processed using the three types of processing techniques, namely histogram, Otsu thresholding and morphological function.

3.3. Otsu thresholding method The acquired image is first converted in to a grey image. The red component is extracted from the image and filtered using a median filter.

3.3.1.

Median filter The median filter is used in this method for preserving the edges and to remove the salt and pepper noise. The process of the median filter is the replacing of each value with the median value of the neighbouring pixel. It is determined by first arranging all the pixel esteems from the window into numerical request, and after that supplanting the pixel being considered with the centre pixel esteem.

The focal pixel is supplanted by the middle of pixels in the window.

3.3.2.

Histogram equalization The image is processed using histogram equalization which helps adjust the image intensities to enhance contrast.

Table 2. Histogram analysis No 1 2 3 4

Image Image 1 Image 2 Image 3 Image 4

Histogram value No peaks in the ranges less than 150 Medium pixel values in <150 region less pixel value in <150 region More pixel values (peaks) in <150 region

Observation Side wall Moderate leak Light leak severe leak

3.3.3.

Otsu thresholding In image binarisation, Otsu threshold method helps to reduce the grey level image to a binary image.

In this method, all possible threshold values are iterated to calculate the spread of pixel value on both sides of the threshold value. The purpose is to find out the threshold value for classifying the is either leaking or nonleaking. According to OTSU method, minimizing ‘within-class variance' is equal to maximizing ‘between class variance' of the segmented image. However, maximizing ‘between class variance' is less expensive than minimizing ‘within-class variance'.

3.4. Morphological process 3.4.1. Gabor filters The RGB image is converted to a grey image. The captured image is filtered using a Gabor filter.

The Gabor filter converts the image to discrete value for getting the constant region of the image. Compared to other filters, the Gabor filter is primarily used for a low range of values. The filtered image is iterated for five iterations. This helps extract particular affected area for a clearer view.

3.4.2.

Enhancement techniques In this technique, there are two types - dilation and erosion. Dilation is the process which increases the white content of the image by adding pixels to the boundary of the object. Erosion is used to increase the black content of the image by removing the pixels from the boundary of the object. The small particles in the dilated image are eroded in the erosion process.

3.4.3.

Morphological function A morphological method identifies the shape and features of a cold air leaking area in a given image. The organizing component is set in every conceivable situation in the picture and it's contrasted and the relating neighbourhood of pixels. According to the output of the test, it identifies the image that fits or hits the neighbourhood pixels. Erosion strips one layer of both inner and outer boundaries. It avoids small Application of thermal imaging for detecting cold air leak location in cold storage (P. Pathmanaban)

298



ISSN: 2252-8814

details and enlarges holes and gaps between different regions. The boundaries are found by subtracting the eroded image from the original image. x=f−(f(Θ)s), where f is an image of the regions, s is a 3×3 structuring element, and x is an image of the region boundaries [16]. Dilation adds one layer on both the inner and outer boundaries. It will fill in the gaps between the two regions.

4.

RESULT AND DISCUSSION The analysis is done with four images taken at different location of cold storage. Each image is taken separately and the results are observed using four methods and compared.

4.1. Histogram method According to Figure 2, it was observed that the pixel intensity of image 1 (a) lies between above 150 to 255 range. It is due to a high number of bright sides by high temperatures. It is noted as no leak of cold air.

At the same time pixel intensity of image 2 (b) has shown a light peak in the histogram at the range less than 150. It is due to darker regions of images caused by low temperatures. Similarly, the histogram of image 4 (d) shows more peaks in the lower range and image 3 (c) has a moderate peak in lower ranges. It is observed that the histograms show the cold leaking regions clearly by the distribution of pixel intensities.

(a)

(b)

(c)

(d) Figure 2. Histogram of the images Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 294 – 301

Int J Adv Appl Sci

ISSN: 2252-8814



299

4.2. Otsu thresholding method According to Figure 3, the same images which has been used in the histogram method are processed with otsu thresholding. The result shows the image 4 (d) having more leaking regions than others.

Image 1 (a) shows no leaking, similarly the images 2 (b) and image 3 (c) showing less and moderate leaking respectively.

(a)

(b)

(c)

(d) Figure 3. Thresholding method of images

4.3. Error diffusion dither Same images were also used in this thresholding. After histogram equalization of all the images, error diffusion dither thresholding applied to all the images and the results are comparable. In this method, the exact location of the leak was identified. Image 4 Figure 4. shows more leaking region than other images.

Application of thermal imaging for detecting cold air leak location in cold storage (P. Pathmanaban)

300



ISSN: 2252-8814

Figure 4. Error diffusion dither

4.4. Morphological method According to Figure 5, the thermal image (a) is converted into a greyscale image, and the intensity of the image is noted (b). The grey image has undergone five iterations and all the stages are shown in (c).

After iteration, the Fig.ure proceeds with the morphological process. For extracting the leak area, the iterated image analyses with the morphological process (dilation and erosion) (d). The leak location of the image is extracted and it is incorporated with the original image. This helps to identify the leak location without an expert's advice (e).

(a)

(b)

(d)

(c)

(e) Figure 5. Morphological functions

5.

CONCLUSION Thermal images can be used to identify the cold air leak location of cold storage by analyzing the images, which helps to predict early, the cold air leakages and prevent further energy loss. Four methods have been used for the thermal image analysis. It is observed that all four methods are useful. In that, the histogram method helps identifying a leak location. However, the other three methods help in extracting the region of interest by enhancing the leak location. In future, it is recommended that the analysis has to be done by taking images from inside the cold storage and finding the heat transmission into cold storage from outside. Moreover, image classification algorithm should be made for detailed analysis and improve the accuracy of results.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 294 – 301

Int J Adv Appl Sci

ISSN: 2252-8814



301

REFERENCES [1]

[2] [3] [4]

[5] [6]

[7] [8] [9] [10]

[11] [12]

[13] [14] [15] [16]

Barbosa-Cánovas, U.V. , Fernández-Molina, J. J. , Alzamora, S. M. , Tapia, M. S., et al., Handling and Preservation of Fruits and Vegetables by Combined Methods for Rural Areas, Technical Manual FAO Agricultural Services Bulletin 149 Food and Agriculture Organization Of The United Nations Rome, 2003.

Tashtoush, B. “Natural losses from vegetable and fruit products in cold storage,” Food Control, vol. 11, no. 6, pp. 465-470, 2000.

Chen, P., Cleland, D.J.Lovatt, S. J .,Bassett, M.R “Air infiltration into refrigerated stores through rapid-roll doors,” Proc. of the 20th International Congress of Refrigeration, pp. 19-24, Sydney, Australia., 1999.

Ligtenburg, P.J.J.H., & Wijffels. D.J. “Innovative air curtains for frozen food stores,” Proceedings of the 19th International Congress of Refrigeration, pp.

420-437 https://www.researchgate.net/publication/287772728_Innovative_air_curtains_for_frozen_food_stores, 1995.

Foster, A., Barrett, R., James, S., & Swain, M. “Measurement and prediction of air movement through doorways in refrigerated rooms,” International Journal of Refrigeration, vol. 25, no. 8), pp. 1102-1109, 2002.

Evans, J. A., Hammond, E. C., Gigiel, A. J., Fostera, A. M., Reinholdt, L., Fikiin, K., & Zilio, C. “Assessment of methods to reduce the energy consumption of food cold stores,” Applied Thermal Engineering, vol. 62, no. 2), pp. 697-705, 2014.

Tian, S., Shao, S., & Liu, B. “Investigation on transient energy consumption of cold storages: Modeling and a case study,” Energy, vol. 180, pp. 1-9, Aug 2019.

Wang, C., He, Z., Li, H., Wennerstern, R., & Sun, Q. “Evaluation on Performance of a Phase Change Material Based Cold Storage House,” Energy Procedia, vol. 105, pp. 3947-3952, 2017.

Jadin, M. S., & Taib, S. “Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography,” Infrared Physics & Technology, vol. 55, no. 4, pp. 236-245, 2012.

Duan, Y., Huebner, S., Hassler, U., Osman, A., Ibarra-Castanedo, C., & Maldague, X. P. V. “Quantitative evaluation of optical lock-in and pulsed thermography for aluminum foam material,” Infrared Physics & Technology, vol. 60, pp. 275-280, 2013.

Omar, M., Hassan, M. I., Saito, K., & Alloo, R. “IR self-referencing thermography for detection of in-depth defects,” Infrared Physics & Technology, vol. 46, no. 4, pp. 283-289, 2005.

Aggelis, D. G., Kordatos, E. Z., Soulioti, D. V., & Matikas, T. E. “Combined use of thermography and ultrasound for the characterization of subsurface cracks in concrete,” Construction and Building Materials, vol. 24, no. 10, pp. 1888-1897, 2010.

National Horticulture Board, “Technical Standards and protocol for the cold chain in India, Cold chain development centre,” India. http://nhb.gov.in/documents/cs2.pdf.Accesed on June 13,2019, 2010.

Akdemir. S., “Evaluation of cold storage insulation by thermal images analysis,” Bulg. J. Agric. Sci., vol. 20, pp. 246-254, 2014.

Akdemir.S., “Designing of Cold Stores and Choosing of Cooling System Elements,” Journal of Applied Sciences, vol. 8, no. 5, pp. 8788-8794, 2008.

Kumar, U. S., & Sudharsan, N. M. “Enhancement techniques for abnormality detection using thermal image,” The Journal of Engineering, vol. 5, pp. 279-283, 2018.

Application of thermal imaging for detecting cold air leak location in cold storage (P. Pathmanaban)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 302~312 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp302-312



302

Solar energy as a potential contributor to help bridge the gap between electricity supply and growing demand in Iraq: A review Sulala Al-Hamadani School of Architecture and Built Environment, University of Newcastle, Australia School of Planning and Geography, Cardiff University, UK

Article Info

ABSTRACT

Article history:

The pivotal role of electricity is as an enabler for every other sector in any economy. Adequate electricity supply is a vital input for the economic growth and in a range of key industries. Unfortunately, the electricity sector in Iraq has been an unsustainable fiscal burden on successive Iraqi governments yet it has not been able to meet the growing demand. Iraq’s electricity sector and government’s decision makers should look for a longterm solutions and strategies to meet the current and future demand, by taking important steps towards fostering a reliable, affordable, and sustainable electricity system in the years and decades to come. Also, Iraqi government and ministry of electricity in particular should understand that electricity is basic service and right of Iraqis, and many nations have overcome this kind of problem decades ago. Overcoming this problem will help refueling Iraqi economy and enable it to stand on its feet again.

Renewable energy resources, and solar in particular could be part of the solution. This paper aims to highlight the importance of solar energy in Iraq as a potential contributor to help bridge the gap between electricity supply and growing demand. Also, it discusses the solar energy opportunities with challenges facing other renewable energy sources in Iraq.

Received May 27, 2020 Revised Jun 7, 2020 Accepted Jul 3, 2020 Keywords: Electricity Growing demand Renewable sources Solar energy Supply

This is an open access article under the CC BY-SA license.

Corresponding Author: Sulala Al-Hamadani, School of Architecture and Built Environment, University of Newcastle,

University Dr, Callaghan NSW 2308, Australia.

Email: Sulala.AlHamadani@uon.edu.au

1.

INTRODUCTION Globally, there is an increased demand for energy, especially electrical energy. This coupled with the rise of issues such as climate change, fossil fuel depletion, the increase and recent fluctuations in oil prices and their cumulative impact on the economies and renewable energy sources. All of these issues encourage the investigation of using solar, wind and other renewable energies for the generation of electrical power [1-2]. There are many different kinds of renewable energy sources like geothermal, biofuel, tidal and so on, but - as this paper will argue - solar energy is more available and accessible than other kinds in the Middle Eastern countries, and Iraq in particular.

Solar energy is a clean and renewable energy source with potential in many areas around the world.

Solar power generation demand increases worldwide as many countries aim to reach goals for emission reduction and renewable power generations [3]. Solar energy exploitation can be achieved through the solar thermal and solar photovoltaic (PV) routes for various applications [4]. In 2014, the global investment in Journal homepage: http://ijaas.iaescore.com

Int J Adv Appl Sci

ISSN: 2252-8814



303

renewable energy has reached USD 270 billion, accounting for 59% of net additions to global power capacity [5]. As a leading sector, solar energy was the dominant sector, accounting for 55% of total the new investment in non-hydro renewable energy. Most of the investment in the solar energy sector is dedicated to solar photovoltaics (solar PV). This resulted in a cumulative global solar PV installed capacity of 177 GW [5].

Technological advancements in solar PV and the fast decreasing costs mean that unsubsidized solar power is already economically feasible in some regions of the world. The increase in the worldwide expansion of solar PV was dramatically rapid. In 2000, the global total PV installations represented 1.8 GW, and increased to reach 71.1 GW in 2011 with an annual growth rate of 44%. The Global cumulative PV installations reached about 106 GW [6]. The leading countries in solar PV based power are Japan, Germany, the UK, China, Spain and Italy, these countries have managed to integrate PV based power in their electricity production [7]. In 2012, European capacity for PV electricity was 17.2 GW, and it is expected to reach 48 GW by 2017 [8]. The total global solar power capacity is forecasted to grow from 98 GW in 2012 to 308 GW in 2018 [9].

Fortunately, Iraq lies within the high potential locations for solar power plants, these are United State of America south west, Mediterranean European countries, Middle East and Near East, Iran and India deserts, Pakistan, China and Australia [9-10]. The purpose of this paper is to highlight the importance of solar energy as a potential contributor to help meet the growing demand of electricity in Iraq, and compare its potentiality with other sources of renewable energy in Iraq.

OVERVIEW OF IRAQ’S ELECTRICITY STATUS The electricity sector plays a critical role in the socioeconomic development of post-war countries such as Iraq [11-12]. Iraq has experienced three wars, civil unrest, and economic sanctions during the last four decades, which devastated the power system. The rise in Iraqis’ income after the 2003 war has led to an increase in the consumption of electricity for household appliances. Catching up and keeping pace with rising demand for electricity is critical to Iraq’s national development [13]. Power stations in Iraq produce more electricity than ever before, but power cuts are still being experienced on a daily basis in many parts of Iraq.

Despite a considerable growth in grid-based electricity capacity in recent years, where the peak net daily production in 2011 was around 70% higher than 2006, it is still far from being adequate to meet demand.

The net capacity accessible at peak in 2011 was about 8 GW while the estimated requirement of the net capacity to meet peak demand was 15 GW, which means about 7 GW was needed to close the gap between supply and demand. Iraq is in need of around 70% more netpower generation capacity to be able to meet full electricity demand [14].

This supply gap for electricity in Iraq has led to the widespread use of back-up private diesel or petrol generators. Based on a survey conducted by Iraq knowledge network in 2012, about 90% of Iraqi households supplement the public network with private diesel or petrol generators, these generators are either a private household or a shared generator with different sizes and capacities operating at neighbourhood level [15]. These private generators represent a financial burden on both the Iraqi government and its citizens.

Iraqis spent nearly USD 80 billion on purchasing electricity produced from private diesel generators while the Iraqi’s government subsidizes the fuel of these shared generators [16]. Private diesel generators presently play a crucial role in reducing Iraq’s shortfall in electricity supply, they help reduce the number of blackouts.

However, the price of the electricity supplied by these private generators to the Iraqi consumers is significantly higher than grid electricity. Residential customers are paying 10 to 15 times more for the electricity provided from private generators than from the grid. Besides being an expensive and inconvenient method to supply electricity for Iraqis, private diesel generators also contribute to local air pollution [17].

The Iraqi government’s spending on improving and reconstructing the faltering power system was around USD 41.5 billion during the period 2004–2012. That includes USD 17 billion, which was financed from the country’s oil revenue, spent on upgrading existing power stations and setting up new generators.

Despite the efforts of subsequent Iraqi governments to improve the national electricity system, and the Iraqis to try to fill some of the electricity supply gap, the average availability of electricity to consumers (from all sources) was limited in 2011 to about 11–19 hour per day varying across the Iraqi provinces. By May of 2014, the Iraqi government was confident that its citizens would finally benefit from 24 hours of electricity supply based on its plan to add 8 GW of generation capacity to reach 20 GW by the end of 2015 [18].

However, because of national security issues, the ministry of electricity instead announced grid losses of more than 8 GW, where Iraqis were back to having electricity that was provided five to eight hours a day at best, and wary of fresh promises from the ministry that production will recover to 12 GW by summer 2015 [18].

2.

Solar energy as a potential contributor to help bridge the gap between electricity … (Sulala Al-Hamadani)

304



ISSN: 2252-8814

This deep-seated gap between supply and demand is estimated to have caused some US$40 billion in annual losses for the Iraqi economy [19], and the problem looks likely to continue. Based on recent studies, Iraq electricity demand will continue growing where total peak demand is likely to reach anywhere between 50 to 60 GW by 2030, which exceeds the ministry of electricity forecast peak demand, which was about 35 GW as in Figure 1 [19, 20].

Figure 1. Electricity demand forecast in Iraq [19]

3. MAIN CAUSES OF THE GROWTH IN ELECTRICITY DEMAND IN IRAQ 3.1. The rise in gross domestic product The considerable increase in the growth rate for GDP after 2003 was due to a sharp increase in crude oil prices. However, as the world demand for oil changes, and Iraqi economy depends largely on oil export revenues, it will be difficult to accurately forecast the exact growth in Iraq’s GDP. Nevertheless, the annual growth almost exceeded 10 percent in 2012. According to the International Energy Agency (IEA) forecasted estimates, the central scenario for the GDP growth will be 10.6 percent per annum during 2010–2020 and is expected to decline gradually thereafter to settle at around 8.78 percent in the next two decades [21].

3.2. Low electricity tariff As shown in Figure 2, Iraq is among the counties that have the world’s lowest electricity prices [21].

The current tariff levels for the five consumer categories are largely subsidized, ranging between 0.85 and 4.50 US¢/kWh (weighted average is US¢1.85/kWh), which is far from being sufficient to cover the costs and almost eight times lower than the Organization for Economic Co-operation and Development (OECD) countries' average household electricity price in 2010 of 16 US¢/kWh [20]. Based on the Ministry of Electricity website, the cost covering tariff is about 11.30 US¢/kWh. This high cost recovery is mainly resulting from the high technical and non-technical losses in the transmission and distribution system.

The specific analyses as to how much a tariff increase could reduce electricity demand in Iraq is not available. But the low electricity tariff is definitely one of the factors likely to increase the growth of demand based on other countries' experience.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 302 – 312

Int J Adv Appl Sci

ISSN: 2252-8814



305

Figure 2. Cross-country comparison of average residential electricity prices in selected MENA and nonMENA countries, 2008 [21]

3.3.

Population growth and increased housing demand The growth in population is a major contributor in the increased demand for electricity. As of June 2011, Iraq population was 33.3 million, and it is expected to reach 55.85 million by 2030 [22]. It is clear that in the next 20 years, the population will apply huge pressure on the energy resources of the country.

The electrification of urban areas especially the capital Baghdad will continue to increase, as more houses will be required over the coming years with the current continuation of inadequacy in housing of more than 1 million housing units [23]. The predicted number of housing stock will reach 8.4 million units by 2030 with the current average ratio of population to housing stock. It is anticipated that 97 percent of the households will be connected to the electricity grid by 2030 compared with 79 percent in 2012. Hence, the number of customers is expected to double from 3.94 million connected households to 8.40 million in 2030 [23].

As a result, although one might recognize potential for energy efficiency and demand management, these demographic and economic factors are likely to drive upwards overall electricity consumption in Iraq.

IRAQ’S FOSSIL-FUEL DEPENDENCE IN ELECTRICITY GENERATION Iraq is already the world’s third-largest oil exporter [24] and has the resources and plans to increase rapidly its oil and natural gas production as it recovers from three decades punctuated by conflict and instability. Success in developing Iraq’s hydrocarbon potential and effective management of the resulting revenues can fuel Iraq’s social and economic development [25]. Iraq was the world's seventh largest producer of crude oil in 2013 and is currently OPEC’s second biggest producer. Iraq represents a sizeable part of global oil supplies, accounting for about 4 percent of global oil production in the first quarter of 2014 [26].

In 2010, Iraq’s Ministry of Oil increased its figure for the country’s proven reserves to 143 billion barrels, almost 25% more than the previous 115 billion barrels. The new reserve estimate gives Iraq the fifth-largest proven oil reserves in the world and the third-largest conventional proven oil reserves after Saudi Arabia and Iran [27, 28]. According to the IEA, Iraq produced roughly 3.4 million b/d in May 2014 [29]. The IEA’s medium-term outlook forecasts that Iraqi production could reach 4.8 million b/d by 2018. Most of Iraqi oil reserves are still unexploited which may make Iraq a leading country in the world oil market in future. Iraq’s potential to increase oil production in the coming decades is seen by many analysts as a key component to both Iraq and global growth. According to the IEA's 2013 World Energy Outlook forecasts, Iraqi crude and Natural gas liquids (NGLs) production to rise to 5.8 million b/d by 2020 and to 7.9 million b/d by 2035 in the base case scenario, making it the single largest contributor to global oil supply growth through 2035 [18].

If Iraq manages to increase its oil production exponentially, in the next decades, Iraq can beat Saudi Arabia in oil production and exportation.

Iraq’s economy is largely dependent on the oil sector, which generates more than 95 percent of export earnings and more than 60 percent of the country’s GDP [30]. Since Iraq has very rich fossil energy resources, little attention has been paid so far to explore alternative energy production and renewable resources for electricity production. As in Figure 3, electricity generation in Iraq is largely dependent on fossil fuels, which account for about 86.3 percent of the main grid electricity source, meanwhile the share of renewable energy is 13.7 represented by only hydropower [20].

4.

Solar energy as a potential contributor to help bridge the gap between electricity … (Sulala Al-Hamadani)

306



ISSN: 2252-8814

Diesel, 8.83%

Hydro, 13.71% Gas turbine, 47.70% Thermal, 29.75%

Figure 3. Iraqi electricity generation profile [20]

5.

SOLAR ENERGY IN IRAQ Aside from hydroelectricity, other forms of renewable energy have not been adopted on a large scale due to the fact that oil is relatively cheap and easily accessible in Iraq. There are no incentives to look for alternative forms of energy at this time. In addition, protecting the environment is not a top priority in Iraq due to the rise of many other urgent issues such as fighting terrorism and plunging oil prices. But the electric power needed in Iraq is not enough to meet the power demand of domestic and industrial sectors. In addition, the electricity sector can help Iraq’s economy to stand on its feet again, which in return achieves the Iraqi’s longing for long-awaited peace and prosperity. Given this, there is a strong case to search urgently for alternative energy sources, particularly potential resources for renewable energy, such as solar and wind, which are clean, inexhaustible, and environmentally friendly [31]. Among the non-hydro renewable energy sources in Iraq, solar energy as an alternative energy to conventional means of electricity generation has the strongest potential. Indeed, as many other nations in the Middle East, interest in solar energy has increased in Iraq [32]. According to the country’s energy master plan, around 24.4 GW of new capacity will be added due to 2017, with 0.4 GW of renewable energy, mainly solar energy [33]. There are various factors indicating that solar energy is the most suitable source of renewable energy in Iraq, which are discussed in more detail as follows.

5.1. Excellent solarity and geography of Iraq In Iraq, they say “safya dafya ‫ ”صافيه دافيه‬meaning everything is fine – literally, “sunny and warm.”

Safya dafya could also characterize the forecast for solar power development in Iraq, as the nation’s ample sunshine and flat, open land near population centres make Iraq one of the best locations in the world for solar power [34]. Iraq is known for long hours of sunshine. Studies have shown that Iraq receives more than 3000 hours of solar radiance per year in Baghdad alone [33]. The range of the hourly solar intensity varied between 416 W/m2 in January to 833 W/m2 in June. Even the hours of sunshine observed in Spain cannot compete with the levels in Iraq [32]. Iraq is located at the south-west of Asia. Iraq lies between latitudes 29 50 and 37 220 north, and between longitudes 38 450 east and 48 450. The area of Iraq is 435052 km 2.

The investigation of global solar radiation intensity in Iraq has been conducted by many researchers [35, 36].

Abdul-Wahid and Hassan [37] have examined different sites in Iraq, those are in the southern region consist of Al-Basrah, Al-Nasriya, AlSamawa, and Al-Amara, the middle consists of Baghdad, Haditha, Al-Rutba, Kerbala, Al-Hai, Al-Najaf and Al-Diwaniya and the northern part of Iraq includes Kirkuk, Khanaqin, Sulaymania, Al-Mosul and Zakho. As can be seen from the Figure 4 that the net radiation in southern and middle regions is higher than in the northern part of Iraq.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 302 – 312

Int J Adv Appl Sci



ISSN: 2252-8814

307

Figure 4. The net solar radiation (a) (southern region of Iraq), (b) (middle to west of Iraq) and (c) (northern region of Iraq) [37]

The monthly mean of daily solar radiation, sunshine duration, maximum temperature and relative humidity were collected from the archives of Iraqi meteorological office [38]. The daily data collected through five years from Baghdad, Mosul and Ar-Rutbah which represented middle, north and west of Iraq as in Table 1. As presented in Table 1, it is clearly noted that the highest radiation is at June, July and August, while January and December are lowest one. Meanwhile, Table 2 demonstrates the outcomes of estimated Sunshine and global solar radiation of three areas situated at the west and north of Iraq; Haditha, Beji and Samara, these outcomes have been estimated for a period of 17 years [39].

Table 1. Sunshine and global solar radiation estimated from measured meteorological data values for a period of 2004–2008 years [38] Month

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Baghdad 33.22N, 44.23E Sunshine Solar irradiance [hour] MJ/m2 day 5.7 10.6 6.7 13.33 7.9 17.7 9.9 21.6 10.1 23.4 12.6 27.0 12.3 26.0 12.1 24.6 10.5 20.8 9.2 15.8 7.7 11.9 6.3 9.8

Mosul 36.32N, 43.15E Sunshine Solar irradiance [hour] MJ/m2 day 4.6 6.9 5.0 9.9 5.8 13.4 8.1 17.7 10 19.9 12.3 22.8 12 21.3 11.8 21.0 9.7 18.0 7.5 12.4 4.3 7.7 4.2 5.3

Rutba 33.03N, 40.28E Sunshine Solar irradiance [hour] MJ/m2 day 6.0 9.2 8.8 15.5 8.4 18.6 7.9 21.7 9.5 23.4 11.7 25.9 12.3 25.2 11.2 24.9 10.3 22.3 9.2 15.3 7.3 10.9 6.0 9.9

Solar energy as a potential contributor to help bridge the gap between electricity … (Sulala Al-Hamadani)

308



ISSN: 2252-8814

Table 2. Sunshine and global solar radiation estimated from measured data (1995–2012) [39] Month Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Haditha Sunshine MJ/m2 day 5.875 11.91 7.229 15.64 7.954 20.89 8.379 26.25 9.992 30.3 11.95 31.83 11.942 31.86 11.408 29.44 10.267 25.43 8.558 19.37 7 14.64 5.792 10.63

Sunshine 5.765 6.529 6.988 7.871 9.041 11.382 11.265 11.029 10.047 8.135 6.294 5.318

Beji MJ/m2 day 9.64 12.84 16.75 21.38 24.05 27.01 26.72 24.89 21.31 15.94 11.65 8.9

Sunshine 5.876 6.9 7.614 8.71 9.962 11.033 10.652 10.224 8.929 7.867 6.452 5.29

Samara MJ/m2 day 8.41 10.78 14.67 16.21 19.81 21.45 21.9 20.57 17.69 12.78 9.31 7.5

Both Table 1 and Table 2 reveal the status of the sunshine and global solar radiation in Iraq.

The measurements on horizontal surfaces that have the highest values at all considered locations appear in June, while the lowest values were in December, the annual average daily values for the global solar radiation on horizontal surface at Baghdad is 18.57 MJ/m2/ day, at Mosul is 14.75 MJ/m2/day and at Rutba is 18.53 MJ/m2/day. In terms of solarity calculations, Iraq can be divided into three regions the north, the middle and south. These regions have an averaged from 16 to 10 MJ/ m2/day for 5 months in the north, 6 months in the middle and southern region, respectively. But the most attractive and strategic place to build and develop solar plants in Iraq is the Al-Anbar province, the vast desert province in central-western Iraq, which have almost 8 months of sunshine duration while the lowest is over 4 MJ/m2/day. This level of energy is entirely adequate to run all the photovoltaics (PV’s), concentrated solar power (CSP) and houses hold facilities such as heating, cooling and water distillation along the year [14]. Therefore, solarity and geography represent a strong foundation pillar for Iraq’s solar power opportunity and future.

5.2. The reduction in hydropower generation in Iraq.

Hydropower generation represents the only current form of renewable energy in Iraq, accounting for nearly 10% of the electricity generation mix in 2010 (around 5 TWh) [25]. The decline in the precipitation rate projected for the future years and the related reduction in water availability is expected to decrease Iraq hydroelectric production in the future. As shown in Table 3, Iraq hydropower is generated from the dams built on the Euphrates and Tigris rivers and their tributaries [40]. A significant percentage of Iraq’s water resources originates from outside its international boundaries, mainly from Turkey and Iran. Therefore, Iraq is highly vulnerable to the development plans of upstream states. As a result of dams already built on the Euphrates and Tigris, the analysis confirmed that the current annual flow of the Euphrates and Tigris entering Iraq has dropped dramatically [41]. Estimates suggest that the flow of water to Iraq from Euphrates river has been reduced by 20 per cent, and it is suggested that the rivers’ flow could be reduced still further due to Southeastern Anatolia Project in Turkey, known as GAP in Turkish, possibly by 50% [42].

The Southeastern Anatolia Development Project involves the construction of 22 dams and 19 hydroelectric power plants, with an installed capacity over 7.5 GW on the Euphrates and Tigris rivers. This project once completed, will have a tremendous flow impact on the downstream countries, namely, Iraq and Syria [18].

Although this decline in water levels can be attributed to many factors such as dam construction in Turkey and Iran, an increased withdrawal of water for irrigation, population growth and the absence of an effective water resource management in Iraq, years of drought have evidently reduced the runoff in the river basin, especially in the Tigris River. Taking into consideration the predicted decline in precipitation due to climate change, a reduction in the levels of water in the Tigris and Euphrates is expected for the coming years, which could change future hydrologic conditions in Iraq and, as a result, future hydro power generation [43].

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 302 – 312

Int J Adv Appl Sci

ISSN: 2252-8814



309

Table 3. The hydro power plants installed capacity in Iraq [40] Project name Dokan Dam Darbandikhan Dam Mosul Main Dam Mosul Dam pump storage plant Mosul Regulating Dam Haditha Dam Samaraa Barrage Hemrin Dam Adhaim Dam Al-Hindiyah Barrage Shatt Al-kuffa Regulator Total installed capacity Note: Data from 2011

Installed capacity (MW) 400 240 750 200 60 660 80 50 40 15 6 2501

5.3. The limitation of wind energy in Iraq Compared to solar energy, wind energy is too limited in Iraq. The current proposed wind power projects exist in specific locations according to the wind availability there [44]. Al-Azzawi and Zeki [45] conducted a comparative study, comparing the characteristics of wind power resources and solar radiation energy of some meteorological stations in Iraq, the results showed that the wind power potentials in Iraq are relatively small (but not neglected) in comparison with solar energy. Wind power resources increase as one moves from the northern regions towards the southern regions. Another recent study by Hassoon [46] to assess the potential wind energy in the north of Iraq, concluded that the average wind speed is below the minimum speed, 5.0 m/s, needed for wind-electric generation. The highest average wind speed is recorded in July about 4.5 m/s in Tikrit station where an energy of 13.5 kWh/m2 could be extracted.

Although the coastal and offshore environment is windier, Iraq’s Gulf coastline is only 58 km in length (see Figure 5) [47], where the priority is given for oil terminals. Such siting constraints may limit the utilization of offshore wind power, which is considered the most suitable areas for wind energy generation.

Figure 5. Map of Iraq and border nations. (From: www.cia.gov on 10 January 2016) [47]

5.4. Challenges, difficulties and restrictions on the use of nuclear resources Nuclear energy is another low-carbon power generation method that accounts for approximately 20% of world electricity. However, in March 2011, the Fukushima incident changed the public perception of nuclear power, and a result, renewable energy technologies are rapidly gaining ground and nuclear power investment is declining in many states [48]. Generally speaking, nuclear energy has the public perception of being unsafe, and Iraq nuclear history is no exception.

Iraq is a member of the International Atomic Energy Agency (IAEA) since 1959, and began a preliminary program to establish and build a nuclear technology and staff, during its membership of Baghdad Pact, which included: Iraq, Iran, Turkey, and Pakistan [49]. Iraq acquired the first research nuclear reactor in 1967, which started operation with 2 MW energy capacity. It was equipped with facilities for nuclear experiment and research. The location was at the Nuclear Research Center at Tuwaitha, 15 km south Solar energy as a potential contributor to help bridge the gap between electricity … (Sulala Al-Hamadani)

310



ISSN: 2252-8814

of Baghdad [50]. The research use of this reactor was limited to material properties; medicine and agriculture research, but also initiated the thinking of the use of nuclear technology for electrical power generation [51].

In 1981, the completion of Iraqi second reactors with 40 MW thermal capacity was announced, which was constructed and installed by the French Alternative Energies and Atomic Energy Commission CEA and other sources. The second reactor, which was called Tammuz 1 TZ1, was supported by nuclear research facilities.

Unfortunately, this strategic project was seen by two regional countries as a threat, namely, Iran and Israel.

On 30 September 1980, an air strike called Operation Scorch Sword was conducted by the Islamic Republic of Iran Air Force, this attack caused only minor damage to the reactor and wasn't successful in impeding the Iraqi nuclear program [52]. The reactor was fully destroyed about eight months later by Israeli Air Force's Operation Opera carried out on 7 June 1981 [53].

In 1984, Iraq in cooperation with International Atomic Energy Agency (IAEA) planned to build nuclear power plants for electricity generation purpose, relying on 30 years of experience in nuclear technology, including reactor researches [51-54]. The Iraqi Atomic Energy Commission, IAEC signed a contract with Former Soviet Union FSU Atomenergoexport company to construct nuclear power plants for electricity generation. This contract was designed to be executed through stages. The first stage was site selection, followed by the second stage of the program in 1986, involving the studies about the following aspects: topography and geography; seismology and seismotectonics; geology; hydrology and cooling conditions; demography and land use; meteorology and aerology; ecology; electric transmission and grid stability. It also included the undertaking of the task of training personnel for operation and maintenance of the plant by the contractor. It was anticipated that by 2000 Iraq to generate 2 GW of electricity from two nuclear power plants built on two sites, which was supposed to be chosen from the four suggested sites: Al-Mahzam, AI-Abasia, Abu-Dalaf and Baiji [55].

Following the end of the Gulf War in 1991, UNSC Resolution 687 directed the IAEA to find and dismantle Iraq's nuclear program, and guarantee Iraqi compliance with the Non-Proliferation Treaty or NPT through comprehensive ongoing monitoring and verification. Between May 1991 and October 1997, the IAEA finished a series of 30 inspection campaigns, supervised the destruction and disablement of nuclear facilities, and removed all weapons-usable nuclear material from Iraq [56]. In 2009, Ministery of Science and Technology announced that Iraq would explore the feasibility of developing a peaceful nuclear program for research purposes and electricity generation, considering the growing demand for electricity [57]. However, domestic instability and cost considerations halted Iraqi government plans from pursuing nuclear power or other nuclear technologies at that time. Currently, Iraq's nuclear capabilities are limited to medical and agricultural applications, which is a normal result of years of inspections, sanctions and conflicts. All of the above-mentioned challenges do not encourage the use of nuclear power as a source of renewable and low-carbon energy in Iraq [58].

6.

CONCLUSIONS The impact of wars, of underinvestment in the power sector, and rapidly growing electricity demand have created a shortage of—and desperate need for - a reliable and environmentally friendly source of power in Iraq. Renewable energy resources, such as solar, wind, and hydro are potential solutions to the Iraq electricity growing demand. The potential for utilizing renewable energy sources for electricity production in Iraq is found to be limited in comparison with solar. The solar energy density in Iraq is among the highest in the world. But solarity and location are only one aspect of Iraq’s solar power opportunity. Therefore, it becomes necessity for significant investment to develop the solar energy infrastructure in terms of shared or localized plants with viable storage capacity to help meet the growing electricity demands in Iraq.

In addition, the country’s electricity sector policies need to be reformed, and are required to promote the deployment and utilization of renewable energy, and solar in particular.

REFERENCES [1] [2] [3]

[4] [5]

Kazem Hussein, “A. Renewable Energy in Oman: Status and Future Prospects,” Renewable and Sustainable Energy Review;vol. 15, no. 8, pp. 3465–3469, 2011.

Chaichan M T, Kazem Hussein A., “Thermal storage comparison for variable basement kinds of a solar chimney prototype in Baghdad - Iraq weathers,” International Journal of Applied Science (IJAS), vol. 2, no. 2, 2011.

Hosenuzzaman M, Rahim NA, Selvaraj J, Hasanuzzaman M, Malek ABMA, Nahar A. “Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation,” Renewable Sustainable Energy Rev,;vol. 41, pp. 284–297, 2015.

Solangi KH, Islam MR, Saidur R, Rahim NA, Fayaz H., “A review on global solarenergy policy,” Renewable Sustainable Energy Rev, vol. 15, pp. 2149–2163, 2011.

Lux Research, Losing Power: Evaluating the Drivers and Impacts of Module Degradation, 2014.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 302 – 312

Int J Adv Appl Sci [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

[19] [20] [21]

[22]

[23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33]

[34] [35] [36] [37] [38] [39]

ISSN: 2252-8814



311

EPIA, Global market outlook for photo-voltaic 2013–2017, European Photovoltaic Industry Association; 2013.

Celik AN., “Present status of photovoltaic energy in Turkey and life cycle technoeconomic analysis of a gridconnected photovoltaic-house,” Renewable Sustainable Energy Rev, vol. 10, no. 4, pp. 370–387, 2006.

Masson G., Latour M., Rekinger M.,Theologitis I.T., Papoutsi M., “Global marketoutlook for photovoltaics 2013– 2017,” [Online] Available: www.epia.org/news/publications/.

Devabhaktuni V, Alam M, Depuru SSSR, GreenII RC, Nims D, Near C., “Solar energy: trends and enabling technologies,” Renewable Sustainable Energy Rev, vol. 19, pp. 555–564, 2013.

Inman RH, Pedro HTC, Coimbra CFM, “Solar forecasting methods for renewable energy integration,” Prog Energy Combust Sci, vol. 39, pp. 535–576, 2013.

Lewis, D., “Iraq electricity: the untold story,” Power Econ, 4 Nov, 2004.

Lewis, D.,.”Iraq’s other power struggle,” IET Power Eng, 18–21 Oct/Nov, 2006.

Ministry of Planning- Republic of Iraq (MOP), National Development Plan forthe Years 2010–2014, Ministry of Planning of Iraq, Baghdad, 2010.

Ali A. Kazem , Miqdam T. Chaichan, Hussein A. Kazem, “Dust effect on photovoltaic utilization in Iraq: Review article,” Renewable and Sustainable Energy Reviews, vol 37, pp. 734–749, Sep 2014.

IKN (Iraq Knowledge Network), Iraq Knowledge Network Survey 2011, Iraq Central Statistics Organization/Kurdistan Region Statistics Organization/United Nations, Baghdad, 2012.

Hassoun, N., Al Monitor, Retrieved from: http://www. Al monitor.com/pulse/politics/ 2012/08/iraq-accusationsofsquandering.html, Aug 2012.

Brinckerhoff P., Final report on the survey of private generation in the Baghdad Governorate, Baghdad: Parsons Brinckerhoff; 2009.

Istepanian, H. Iraq's electricity: from crisis to ISIS, Powerengineeringint.com, [online] Available: http://www.powerengineeringint.com/articles/print/volume-22/issue8/features/iraq-s-electricity-from-crisis-toisis.html, 2014.

Al-Khatteeb, L. and Harry, H. “Mean replace it with no number Harvard one,” [online] Available at: http://TURN A LIGHT ON: ELECTRICITY SECTOR REFORM IN IRAQ, 2015.

Istepanian, H. “Iraq's Electricity Crisis,”The Electricity Journal, vol. 27, no. 4, pp.51-69, 2014.

El-Katiri, L., Fattouh, B., and Segal, P. “Anatomy of an Oil-Based Welfare State: RentDistribution in Kuwait’, Kuwait Programme on Development, Governance and Globalisation inthe Gulf States, London School of Economics,” [Online] Available: http://www2.lse.ac.uk/government/research/resgroups/kuwait/documents/Fattouh.pdf, 2011.

United Nations, World Population Prospects: The 2010 Revision.

[Online] Available: http://www.un.org/en/development/desa/population/publications/pdf/trends/WPP2010/WPP2010_VolumeI_Comprehensive-Tables.pdf, 2010.

Ministry of Planning, National Development Plan for the Years 2010–2014, Republic of Iraq, Baghdad, 2010.

Workman, D. “Crude Oil Exports by Country,” World's Top Exports, [Online] Available: http://www.worldstopexports.com/worlds-top-oil-exports-country/, 2016.

IEA, “Iraq Energy Outlook – World Energy Outlook Special Report,” International Energy Agency, Paris.

Retrieved from: www. worldenergyoutlook.org, 2012.

Columbia SIPA, Center on Global Energy Policy, ISSUE BRIEF: IRAQ’S OIL SECTOR, 2014.

BP (2012), BP Statistical Review of World Energy 2012, BP, London.

O & GJ (Oil and Gas Journal), “Worldwide Look at Reserves and Production,” Oil and Gas Journal, vol. 109, no. 49, Pennwell Corporation, Oklahoma City, United States, 2011a.

International Energy Agency , “Fact Sheet for Oil Supply in Iraq,” [Online] Available : http://www.iea.org/media/Factsheet_OilSupplyInIraq.pdf, 2014.

Baker, J.A., Hamilton, L.H., The Iraq Study Group Report: The Way Forward – A New Approach, Vintage Books: A Division of Random House, Inc., New York, 2006.

Ekren O, Ekren BY, Ozerdem B., “Break-even analysis and size optimization of a PV/wind hybrid energy conversion system with battery storage – a case study,” Applied Energy, vol. 86, no. 78, pp. 1043–1054, 2009.

Kazem Hussein A, and Miqdam T. Chaichan, “Status and future prospects of renewable energy in Iraq,” Renewable and Sustainable Energy Reviews, vol. 16, pp. 6007–6012, 2012.

Petter Pilesjo, Sameer Sadoon Al-Juboori, “Modelling the Effects of Climate Change on Hydroelectric Power in Dokan, Iraq,” International Journal of Energy and Power Engineering, Special Issue: Modeling and Simulation of Electric Power Systems and Smart Grids, vol. 5, no. 1-2, pp. 7-12, 2016.

Doyle, P. and Jaafar, K. “Iraq has an opportunity to become a solar leader,” [Online] Available: http://www.iraqi datepalms.net/Uploaded/file/SolarEnergyLeaderDAIDevelopmentsarticle.pdf, 2010.

Abbas MA, Elnesr MK., “Cloudiness and estimation of incoming solar radiation in Iraq,” Pure Appl Geophys, vol. 112, pp. 234–239, 1973.

Abed FM, Mohammed GHY, “Empirical models for the correlation with sunshine duration of metrological data for Tikrit-TuzKhurmato and KirkukIraq,” Tikrit J Eng Sci, vol. 19, pp. 1–13, 2012.

Abdul-Wahid SN, Hassan AM, “Calculation and applications of net solar radiation in Iraq,” J Al-Qadisiyah Pure Sci, vol. 15, pp. 1-30, 2010.

Al-Salihi AM, Kadum MM, Mohammed AJ., “Estimation of global solar radiationon horizontal surface using routine meteorological measurements for different cities in Iraq,” Asian J Sci Res, vol. 3, pp. 240–248, 2010.

Abed FM, Al-Shahery GhYM, “Estimation of global solar radiation on horizontal surfaces over Haditha, Samara, and Beji, Iraq,” Pac J Sci Technol, vol. 11, pp. 73–82, 2010.

Solar energy as a potential contributor to help bridge the gap between electricity … (Sulala Al-Hamadani)

312



ISSN: 2252-8814

[40] Liu, H., Masera, D. and Esser, L., eds. “World Small Hydropower Development Report 2013,” United Nations Industrial Organization; International Center on Small Hydro Power, [Online] Available: www.smallhydroworld.org, 2013.

[41] UNESDOC, Integrated drought risk management – DRM national framework for Iraq - executive summary, report from: UN Educational, Scientific and Cultural Organization, 13 Oct 2013.

[42] Kurdish Human Rights Project, The Ilisu Dam Campaign, and The Corner House, Downstream Impacts of Turkish Dam Construction on Syria and Iraq: Joint Report of Fact-Finding Mission to Syria and Iraq, [online] Available: www.thecornerhouse.org.uk/sites/thecornerhouse.org.uk/files/IraqSyri.pdf, 2002.

[43] Pilesjo, P. and Al-Juboori, S. “Modelling the Effects of Climate Change on Hydroelectric Power in Dokan, Iraq,” International Journal of Energy and Power Engineering., vol. 5, no. 2-1, pp.7-12, 2015.

[44] Alrikabi, N. “Renewable Energy Types,” Journal of Clean Energy Technologies, vol. 2, no. 1, pp. 61-64, 2014.

[45] S.I. Al-Azzawi, N.A. Zeki, “Comparison between the characteristics of wind power calculations and solar radiation energy of some meteorological stations in Iraq,” Solar & Wind Technology, vol. 4, no. 4, pp. 513-516, 1987.

[46] Ahmed F. Hassoon, “Assessment potential wind energy in the north area of Iraq,” International journal of energy and environment, vol. 4, no. 5, pp. 807-81, 2013.

[47] CIA World Factbook, “Iraq country study,” Central Intelligence Agency, [Online] Available: https://www.cia.gov/library/publications/the-world-factbook/geos/iz.html, 2016.

[48] S.-h. Park, W.-J. Jung, T.-H. Kim, S.-Y.T. Lee, “Can Renewable Energy Replace Nuclear Power in Korea? An Economic Valuation Analysis,” Nuclear Engineering and Technology, vol. 48, no. 2, pp. 559-571, 2016.

[49] Selbi D, Al-Chalabi Z, Khadduri IY, Untold milestones in the Iraqi national nuclear program 1981–1991, Beirut: Arab Scientific Publishers, 2011.

[50] Ahmed SA, Omer MA, Abdulahad AA., “Analysis of wind power density in Azmar mountain (Sulaimani RegionNorth Iraq),” Eng Res Appl, vol. 2, pp. 1403–1407, 2014.

[51] Abdulkhaliq H, Alhijjaj A., The strategy of the nuclear program in Iraq withinthe context of science and technology policies, Beirut: Center for Arab Studies, 2011.

[52] Neil Narang, Erik Gartzke & Matthew Kroenig, Nonproliferation Policy and Nuclear Posture: Causes and Consequences for the Spread of Nuclear Weapons, Routledge Press, 2015 [53] Tucker, Spencer C. The Encyclopedia of the Arab-Israeli Conflict: A Political, Social, and Military History, Santa Barbara: ABC-CLIO, pp. 755, 2008.

[54] IAEC Iraqi Atomic Energy Commission, Annual report 1981, [Online] Available: http://www.iraqwatch.org/government/Index_Iraq.htm, 1981.

[55] IAEC Iraqi Atomic Energy Commission, Annual report 1985, [Online] Available: http://www.iraqwatch.org/government/Index_Iraq.htm, 1985.

[56] Mohamed ElBaradei, The Age of Deception: Nuclear Diplomacy in Treacherous Times, New York: Metropolitan Books, pp. 31, 2011.

[57] Martin Chulov, Iraq goes nuclear with plans for new reactor programme, The Guardian, Oct, 2009.

[58] U.S. Department of State, U.S. and Iraq Sign a Joint Action Plan to Combat Nuclear and Radioactive Smuggling, Press Release, Sep 3, 2014, [Online] Available: www.state.gov.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 302 – 312

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 313~325 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp313-325



313

Efficiency of bond graph and external model integration for alarm processing of a central air conditioning system Abderrahmene Sallami, Dhia Mzoughi, Hatem Allagui, Abdelkader Mami Laboratoire d’Application de l’Efficacité Énergétique et des Énergies Renouvelables-LAPER, Université de Tunis El Manar Campus Universitaire Farhat Hached, Tunisia

Article Info

ABSTRACT

Article history:

The design of a supervision system based on the external model by structuring the industrial process according to several modes of operation (degraded and normal). The disadvantage of this model is that it describes the industrial process components as functions regardless of their dynamics without going into detail. Hence the interest of the bond graph model to fill the external model limits. The performance of the proposed supervisory system using both models lies in the detection and location of faults for each mode of operation. The bond graph model enriched by the concept of causality and thanks to these structural properties can clearly display the elements of the physical system taking into account their dynamics in normal and abnormal operation. The results of our research have been applied to central air conditioning system; the development of the proposed project has proceeded from the modeling stage to the reconfiguration stage of the system.

Received May 31, 2020 Revised Jun 7, 2020 Accepted Jul 3, 2020 Keywords: Bond graph model Central air conditioning system External model Industrial process Supervision system

This is an open access article under the CC BY-SA license.

Corresponding Author: Abderrahmene Sallami, Laboratoire d’Application de l’Efficacité Énergétique et des Énergies Renouvelables-LAPER, Université de Tunis El Manar Campus Universitaire Farhat Hached, 1068 Tunis, Tunisia.

Email: abderrahmenesallami@gmail.com

1.

INTRODUCTION In order to cope with the need of the industrial market and the increasingly severe economic competition, industrialists tend increasingly to expand their facilities and make them more complex. Indeed, this complexity makes the control systems of industrial systems more difficult and specially to reduce safety and increase the risk of breakdowns [1-4]. In addition, supervisory systems use intelligent systems that provide the user with assistance in managing their urgent alarm tasks in order to increase the reliability and dependability of processes [5-10]. However, the improvement of the dependability of the systems is essentially based on fault detection and isolation algorithms [11-19], these algorithms have the role of comparing the actual behavior of the system wither ferrous behaviors describing the normal operation.

The degradation of the performance of diagnostic algorithms is mainly due to the imperfect knowledge of the parametric values of the models and to their random variations [20]. Our research in this paper consists in using a new methodology for the supervision of industrial systems using the external model and the bond graph model. Supervision from the external model point of view considered an industrial system for an operator as an entity offering services, which manipulate variables and use a set of resources [21-24].

The execution of a service is triggered by the verification of an activation condition, and requires the availability of a certain number of resources. At a given moment, this process is in a given mode of exploitation. Only the services belonging to this mode can be executed, from where each mode of operation Journal homepage: http://ijaas.iaescore.com

314



ISSN: 2252-8814

must comprise a specific service of change of mode. However, we cannot join any mode from another for reasons of security and consistency of operation. The problem to solve is to use a unifying tool to integrate the functional and structural models for the design of the supervision system (monitoring and reconfiguration). The bond graph tool is well suited for this task [25-30]. From a monitoring point of view, the causal and structural properties of the bond graph are used for the detection and isolation of faults affecting actuators, sensors or the physical components of the system. Thus, the availability of services, necessary for the accomplishment of a mission, will be provided by the monitoring algorithm to the operating mode management graph. Also, these services defined by functions can be generically to bond graph elements. From a reconfiguration point of view, the graphic properties will be exploited to be able to develop the means of reconfiguration of the system.

2.

SUPERVISION INDUSTRIAL PROCESS BY EXTERNAL AND BOND GRAPH MODELS

2.1. External model

External models structure the industrial process, according to several modes of operation (gradients and normal). Switching from one mode to another operating mode is described by a diagram called a management diagram of the mode of use. It represents the human-machine interface system. Industrial systems consist of a set of equipment interconnected with each other. A hardware failure of one or more of these elements may dare to drive some of the purposes for which the system was designed, and then its hold alert users to generate alarms. The last must be sufficiently synthetic to clearly express to the operator the nature of the failure and its consequences. The external model that is proposed by [31] is considered here.

This model is based on concepts of services, missions and operation modes.

The industrial systems consist of a set of equipment (heat exchanger, motor pump ...), which are organized so that the systems can achieve the objectives for which they were designed: − The low level: These are the basic services; they are directly interfaced with the process (pumps, valves, sensors ...). − High level: These are composite services; they are composed of basic services (cooling, boiler ...).

All services S, that are offered by equipment is defined: S𝑖 = {𝑖∈ 1 …. 𝑛}.

Basic services (low level) are associated with each other in defining compound services and perform a mission (Figure 1). A hardware failure means the unavailability of some basic services.

Si: service i − Mission M1: S1, S4, S5, S6. − Mission M2: S2, S4, S7, S8. − Mission M3: S3, S4, S6, S7, S9, S10. − Mission M4: S1, S4.

Figure 1. Services and missions Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 313 – 325

Int J Adv Appl Sci

ISSN: 2252-8814



315

In this example we find ten (10) services and four (4) missions. Each mission has several services and the S4 service is in all four missions. The missions direct and manage the systems in accordance with the specifications. But at some point, only a subset of these missions is needed to achieve the goals. Each of these subsystems is called a mode of operation or operation. An operating mode (OMi) is a set of services represented by the following relation: Omi = (S1n, S2n, ... Snn) The operation modes are given by a general classification of the operating modes of the industrial devices: off-farm, configuration, manual or automatic mode, nominal ... etc.

The supervision of industrial processes with the external model is determined by the management of the modes of operations, these modes of operations are interconnected to perform a well-defined function.

The request to switch from one mode to another mode must be placed for security reasons because the system may fail on an OMj mode of use when some services are not available. The switch over is represented by a boolean variable bij. The set of operating modes and the bij switching conditions are described by a graph named operation mode management (Figure 2).

Figure 2. Management of operating mode by external model

2.2. Integration of bond graph and external models The integration of the bond graph model with the external model for the supervision of industrial processes is proposed by Ould Bouamama [32], this combination will be linked to the services, missions and modes of operation.

The services as defined in the external models are represented by elements of the bond graph are the services offered by the power source equipment (mechanical: motor), (thermal: heat resistance) and (hydraulic pump) water). The services offered by the functional role of the equipment (storage, processing, transport, etc.) are represented in the bond graph by the basic elements respectively (the capacity C for the potential storage, the restriction R for the processing of air or water, transformer TF for transforming electrical voltages or mechanical torques). It should be noted, however, that the services in jump graph can be quantified by constitutive equations deduced from the bond graph model. Missions represented by sets of services are intended to meet all the objectives set by the specifications, so in the sense of bond graph are the set of basic elements that provide a well-defined mission according to the objectives set the industrial process.

The Modes of operations determined by the external model MEi are the same modes that will be represented by the MBGi model, from which each mode of operation OMi corresponds to a mode of operation must be modeled by a model graph bond MBGi: OMi. At a given time, the method operates in a nominal mode of operation represented by the external model MEfn corresponds to a model bond graph corresponds MBGfn. Let bij denote the requirement to move from MBGi to MBGj (Figure 3).

Efficiency of bond graph and external model integration for alarm processing of … (Abderrahmene Sallami)

316



ISSN: 2252-8814

Figure 3. Management of operating mode by bond graph mode

3.

SUPERVISION OF A CENTRAL AIR CONDITIONING SYSTEM BY EXTERNAL AND BOND GRAPH MODELS 3.1. Description central air conditioning system The central air conditioning system (CACS) provides refrigeration for room auxiliaries for medical experiments in a hospital by circulating a liquid whose temperature is maintained at “10°C”. The circulation of this cooled liquid is ensured by a valve (VC) (see Figure 4).

The water is cooled by the three heat exchangers supplied in parallel (CACS1, CACS2 and CACS3), the temperature of the refrigerant is measured at the inlet and the outlet of the exchangers by the sensors TInp and TOutp.

The valve (VH) is used to modulate the flow of water according to which one, two or three exchangers are in service. Maintaining the temperature at 10°C is provided by the exchanger valves (V12, V22 and V32) connected in series with each exchanger. The valves (V11, V21 and V31) are used to supply the cooling systems under normal operating conditions (two cooling systems, on operation (three cooling systems) or reduced operation (one cooling system).

In normal operation, the circuit is provided by two exchangers in use. During the summer can we add third exchanger. While during the winter only one exchanger can accomplish the operation and also to have gains in the level of electrical energy consumption.

After a total stop (maintenance of the circuit, emptying ...), the restart will be done in a progressive way, that is to say so as to catch the minimum temperature of 10°C.

Figure 4. Schematic diagram of central air conditioning system Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 313 – 325

Int J Adv Appl Sci

ISSN: 2252-8814



317

3.2. System analysis by external and bond graph model in normal function The central air conditioning system consists of a set of equipment (exchangers, tarpaulins ...) that are organized in such a way that the system can meet the objectives for which it was designed.

This equipment is arranged in two ways: − Single component: they are directly interfaced with the process (pumps, valves, sensors). − Complex component: they are composed services; they are composed by basic services (cooling circuits, water booster unit).

According to this principle, it is possible to give a pyramidal decomposition of the cooling system (Figure 5).

Figure 5. Decomposition of the central air conditioning system

The highest-level services are called missions. They must make it possible to satisfy all the objectives set by the specifications. The various missions of the CACS system are as follows: − Mission M1: Put the water in the circuit CACS; − Mission M2: Check the performances of the devices; − Mission M3: Set the bypass of the CS circuit (valves V12, V22 and V32); − Mission M4: Reach the set temperature 10°C; − Mission M5: Ensure the cooling of the auxiliaries by using the pumps (input by one pump P11 and one output by one pump P21) operating at 100%; − Mission M6: Ensure the cooling of the auxiliaries by using the pumps (input by tow pumps P11 and P12, output by tow pumps P21 and P22) operating at 150%, optimally on very hot days; − Mission M7: Ensure the cooling of the auxiliaries by using the pumps (input by one pump P11 and output by one pump P21) operating at “50%” according to the required configuration; − Mission M8: Ensure minimal cooling when the unit is shut down; − Mission M9: Maintain the circuit; − Mission M10: emptying the whole system.

The missions of the cooling system are the ones responsible for leading and managing the system in accordance with the objectives of the specifications. But at a given moment, only a subset of these missions is necessary to meet the objectives set. Each of these subsets is called the operating mode.

For this cooling system, seven operating modes can be distinguished are (Figure 6): − Preparation mode (ME1): The circuit is put in water and a set of tests must be executed; − Start mode (ME2): The water temperature is gradually brought to the set temperature. − Nominal operating mode (ME3): Refrigeration is provided by two central air conditioning system and one pump operating at 100%; Efficiency of bond graph and external model integration for alarm processing of … (Abderrahmene Sallami)

318 − − − −



ISSN: 2252-8814

Reduced operating mode (ME4): Refrigeration is provided by one central air conditioning system and a one pump operating at 50%; Operating mode (ME5): Operation with three refrigeration systems and two pumps operating at 150%; Normal shutdown mode (ME6): The slice is stopped but the set temperature is maintained by a minimum circulation; Full stop mode (ME7): Coolant circulation is stopped and draining can be ensured.

Figure 6. Management of operating mode by external model for central air conditioning system

In this mode of operation (ME5: MBG5), the refrigeration of auxiliaries is ensured by the circulation of the liquid through two pumps (input by tow pumps P11 and P12, output by tow pumps P21 and P22) operating at 150%. The bond graph model is given in Figure 7, this model presented clearly shows the physical phenomena that take place there with three cooling systems (CACS1, CACS2 and CACS3).

In this mode of operation (ME3:MBG3), the refrigeration of the auxiliaries is ensured by the circulation of the liquid thanks to one pump operating at 100%. The bond graph model is given by Figure 8, this model presented clearly shows the physical phenomena that take place there with two cooling systems (CacS1 and CacS2).

In this mode of operation (ME4: MBG4), operation with one pump operating at 50% and an exchanger in service are allowed. The bond graph model is given in Figure 9.

To determine the analysis of any indstrial systems we must calculate the residuals, in our case the residues are as follows: a. Residue r1 : r1 =

b.

1 .( MSe De1 ) RC 1

CF 1 .

dDe1 dt

1 .( De1 RW 1

De3 )

dDe2 dt

1 .( De2 RP1

De1 )

1 .( De2 RP1

De1 )

1 .( De2 RP1

De1 )

(1)

Rsidue r2 : r2 =

1 .( Seext RE 1

De2 )

C P1 .

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 313 – 325

(2)

Int J Adv Appl Sci

ISSN: 2252-8814



319

Figure 7. Bond graph of central air conditioning system for over normal function mode MBG5

Figure 8. Bond graph of central air conditioning system for normal mode MBG3 Efficiency of bond graph and external model integration for alarm processing of … (Abderrahmene Sallami)



320

ISSN: 2252-8814

Figure 9. Bond graph of central air conditioning system for reduced operating mode MBG4

c.

d.

Rsidue r3 : dDe3 1 1 r3 = .( De1 − De 2 ) − CW 1 . − .( De 2 − De1 ) RW 1 dt RP1 Residue r4 : r4 =

e.

g.

j.

CF 2 .

dDe4 dt

1 .( De4 RW 2

De6 )

1 .( De5 RP 2

De4 )

1 .( De5 RP 2

De2 )

1 .( De5 RP 2

De4 )

(4)

1 .( Seext 2 RE 2

De5 )

CP2 .

dDe5 dt

(5)

(6)

Residue r7 : 1 .( MSe RC 1

De1 )

CF 1 .

dDe1 dt

1 .( De1 RW 1

De3 )

1 .( De2 RP1

De1 )

(7)

Rsidue r8 : r8 =

i.

De4 )

Rsidue r6 : dDe6 1 1 r6 = .( De 4 − De5 ) − CW 2 . − .( De5 − De 4 ) R2 dt RP 2

r1 =

h.

1 .( MSe RC 2

Rsidue r5 : r5 =

f.

(3)

1 .( Seext 3 RE 1

De8 )

C P3 .

dDe8 dt

1 .( De8 RP 3

De7 )

1 .( De8 RP 3

De7 )

(8)

Rsidue r9 : dDe9 1 1 r9 = .( De7 − De8 ) − CW 3 . − .( De8 − De7 ) RW 3 dt RP 3

(9)

Residue r10 : 1 1 1 r1 = .( MSe − De1 ) − .( MSe − De 3 ) − .( MSe − De 7 ) RC1 R2 RC 3

(10)

3.3. System analysis by external model in abnormal function If there is a material failure, the cooling system becomes unable to continue part of the missions for which it was designed, the operators of driving and maintenance must be informed. The manufacturers of

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 313 – 325

Int J Adv Appl Sci



ISSN: 2252-8814

321

the cooling system associate nine (09) alarms with the cooling system, they are illustrated in Table 1 and this table gives for each defect a list of services and missions.

Alarm 01A: this fault is associated with the cooling system (CACS1), the missions affected are missions 1, 2, 3, 4, 5 and the operating mode is threatened ME3. a. Mission 1: we find this mission in the operating mode in preparation, the absence of this mission makes the mode in preparation unavailable. If the mode of preparation is the current mode, in the presence of a fault, the automatic transition to another mode must be considered. For this case we find two possibilities: − Start mode: switching to this mode is impossible since it is not available. − Total stop mode: switching to this mode is possible since it is available. b. Missions 2, 3, 4: these missions are found in the startup mode, the absence of this mission makes the start mode unavailable. If the start mode is the current mode, in the presence of a fault, the automatic transition to another mode must be considered. For this case we find two possibilities: − Nominal operating mode: switching to this mode is impossible since it is not available. − Operating mode in preparation: switching to this mode is possible since it is available. c. Mission 5: this mission is found in the operating mode nominal operation; the absence of this mission makes this mode unavailable. If the nominal operating mode is the current mode, in the presence of a fault, the automatic transition to another mode can be considered, for this case we find two possibilities: − Reduced operation: the transition to this mode is impossible since it is not available. − Normal stop operation: switching to this mode is possible since it is available. d. Mission 8: we find this mission in the operating mode normal stop, the absence of this mission makes this mode is unavailable. If the nominal operating mode is the current mode, in the presence of a fault, the automatic transition to another mode can be considered, for this case we find two possibilities: − Reduced operation: the transition to this mode is impossible since it is not available; − Normal stop operation: switching to this mode is possible since it is available.

Table 1. Availability of services and missions in the presence of defects Alarms

Defaults

01A

CACS1 exchanger leak

02A

CACS2 exchanger leak

03A

CACS3 exchanger leak

04A

blocked closed

valve

V11

blocked valve V11 open

05A

blocked closed

valve

V12

blocked valve V12 open

06A

blocked closed

valve

V21

blocked valve V21 open

07A

blocked closed

valve

V22

blocked valve V22 open

08A

blocked closed

valve

V31

blocked valve V31 open

09A

blocked closed

valve

V32

blocked valve V32 open

Services Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min Regulation_temp_max Regulation_temp_min

Missions 1, 2, 3, 4, 5 1, 2, 3, 4, 7 1, 2, 3, 4, 6 1, 2, 3, 4, 5 1, 2, 3, 4, 5 1, 2, 3, 4, 7 1, 2, 3, 4, 7 1, 2, 3, 4, 6 1, 2, 3, 4, 6 1, 2, 3, 4, 5 1, 2, 3, 4, 5 1, 2, 3, 4, 7 1, 2, 3, 4, 7 1, 2, 3, 4, 6 1, 2, 3, 4, 6

Efficiency of bond graph and external model integration for alarm processing of … (Abderrahmene Sallami)



322

ISSN: 2252-8814

3.4. System analysis by bond graph model in abnormal function The alarm 01 corresponding to a fault (leak) at the cooling system (CAS1), this fault causes a decrease in the amount of water cooled. These phenomena are readable on the bond graph model and can be quantified by the equations. However, this faulty component (CACS1) is in the normal operating mode (MBG3), so switching to the reduced operating mode (MBG4) or the normal stop operating mode is allowed.

For the generation of the residues of each mode, we applied the method of the redundant analytical relationships (RRAs), we also call residues. In the case of over normal mode (MBG5) we end up with the following equations of residues: The residual curves (r1, r2, r3, r4, r5, r6, r7, r8, r9 and r10) in over normal operating mode (MBG3) converge to zero, see Figure 10.

When a fault in the cooling system (CACS1), the residues (r1, r2 and r3) are sensitive to this defects.

Or their curves become different to zero at the moment of failure (between 20000s and 40000s) see Figure 11 (evolution of residues) a Figure 12 (evolution of temperatures for each system CACS1, CACS2 and CACS3).

model 0.5

Residue r1

5

Residue r2

5

Residue r3

0.5

Residue r4

5

Residue r5

5

Residue r6

0.5

Residue r7

5

Residue r8

5 -10

Residue r9 0

20000

40000

60000

80000

100000

time {s}

Figure 10. Evolution the residues r1 (t), r2 (t), r3 (t), r4 (t), r5 (t), r6 (t), r7 (t), r8 (t), r9 (t) and r10 (t) for over normal function mode MBG5 without fault

model 0.5

Residue r1

5

Residue r2

5

Residue r3

0.5

Residue r4

5

Residue r5

5

Residue r6

0.5

Residue r7

5

Residue r8

5 -10

Residue r9 0

20000

40000

60000

80000

100000

time {s}

Figure 11. Evolution residues r1 (t), r2 (t), r3 (t), r4 (t), r5 (t), r6 (t), r7 (t), r8 (t), r9 (t) and r10 (t) for over normal function mode MBG5 with fault (leak at the cooling system)

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 313 – 325

Int J Adv Appl Sci



ISSN: 2252-8814

323

model 25

T_frigo 1 {degC}

20 15 10 25

T_frigo 2 {degC}

20 15 10 25

T_frigo 3 {degC}

20 15 10 0

20000

40000

60000

80000

100000

time {s}

Figure 12. Evolution the evolution of the exit temperatures of the CS1, CS2 and CS3 systems for over normal function mode MBG5 with fault (leak at the cooling system)

4.

CONCLUSIONS In this part, we give for the treatment of alarms of the System of intermediate Refrigeration (CACS) specific to each modeling: The external model: Structure the operation of CACS system according to several modes of operation, switch from one mode to another when a mission disappears and evaluate the relevance of the alarm system; The bond graph model: Clearly visualize the physical system (storage, dissipation, transfer as well as the dynamics of process phenomena, find the origins of the alarms by its causal organization, determination and localization of failures by RRA relationships and possibility to use the tools of the external model.

ACKNOWLEDGEMENTS We thank the National School of Engineers of Tunis, University of Tunis El Manar to support our work.

REFERENCES A. Idir1, A. Ahriche, K. Khettab, Y. Bensafia, M. Kidouche, “Real time simulation of sensorless control based on back-EMF of PMSM on RT-Lab/ARTEMIS real-time digital simulator,” International Journal of Advances in Applied Sciences (IJAAS), vol. 8, no. 4, pp. 269-278, 2019.

[2] O.M. Olaniyi, E. Daniya, J.G. Kolo, J.A. Bala, A. E. Olanrewaju, “A computer vision-based weed control system for low-land rice precision farming,” International Journal of Advances in Applied Sciences (IJAAS), vol. 9, no. 1, pp. 51-61, 2020.

[3] Hassan Farhan Rashag, “Improved speed response of DC motor via intelligent techniques,” International Journal of Advances in Applied Sciences (IJAAS), vol. 8, no. 3, pp. 204-207, 2019.

[4] R.Merzouki, A.K.Samantaray, M. Pathak and B. Ould-Bouamama, “Intelligent Mechatronic Systems: Modelling, Control and Diagnosis,” Springer Verlag, ISBN: 978-1- 4471-4627-8, 943 pages, 2013.

[5] T. K. Bera, R. Merzouki, B. Ould Bouamama and A.K. Samantaray, “Design and validation of a reconfiguration strategy for a redundantly actuated intelligent autonomous vehicle,” Journal of Systems and Control Engineering vol. 226, no. 8, pp. 1060-1076, 2012.

[6] A. K. Samantary, B. Ould Bouamama, Model-based Process supervision, Springer, 2008.

[7] K. Medjaher, A. K. Samantary, B. Ould Bouamama and M. Staroswiecki, “Supervision of an industrial steam generator. Part II: On line implementation,” Control Engineering Practice (CEP), vol 14, no. 1, pp 85-96, 2006.

[8] A. Graisyhm, Méthodologie de conception des systèmes de supervision, Rapport Région Nord Pas de Calais, Mai, Valenciennes, France, 1998.

[9] P. Duthoit, J. Martin, La supervision: L’art de maitrise des processus, REE N 7, 1997.

[10] M. Staroswiecki, “La problématique et les approches de la surveillance des systèmes technologiques,” Journées d’Etude S3: sûreté, surveillance, Supervision, Détection et localisation de défaillances. GDR automatique, 1994.

[1]

Efficiency of bond graph and external model integration for alarm processing of … (Abderrahmene Sallami)

324



ISSN: 2252-8814

[11] J. P. Cassar, R. Litwak, V. Coquempot, V. et M. Staroswiecki. “Approche structurelle de la conception de systèmes de surveillance pour les procédés industriels complexes,” JESA, RAIRO-APII, vol. 31, no. 9, pp. 179-202, 1994.

[12] M. Imhemed, B. Conrard, M. Bayart, “Génération de Code grâce au modèle externe pour un instrument intelligent,” 7ème édition du congrès international pluridisciplinaire Qualita 2007, pp. 492-499, Tanger

(Maroc), 20-22 mar 2007.

[13] S. Maza, M. Bayart M, B. Conrard, V. Cocquempot V., “On the dependability design of complex Systems,” 30th ESReDA Seminar: Reliability of Safety-Critical System, SINTEF, Trondheim, Norway Jun 7-8, 2006 [14] M. Bayart, E. Lemaire, M.A. Péraldi, C. André, External model and SyncCharts description of an automobile cruise control, Proc. IFAC Intelligent Components for Vehicle ICV'98, pp. 135-140, Séville, Espagne, 1998.

[15] M. Bayart, E. Lemaire, M. A. Péraldi, C. André, “External model and SyncCharts description of an automobile cruise control,” Control Engineering Practice, vol. 7, no. 10, pp. 1259-1267, 1999.

[16] A. SallamI, N. Zanzouri and B. Ould Bouamama, “Robust Supervision of Industrials Systems by Bond Graph and External Models,” International Journal of Enhanced Research in Science Technology & Engineering, vol. 5, no. 3, 2016.

[17] B. Ould-Bouamama, Contrôle en ligne d'une installation de générateur de vapeur par Bond Graph, Techniques de l'Ingénieurs AG3551, 28 pages, 2014.

[18] A.K. Samantaray and B. Ould Bouamama, Model-based Process Supervision. A Bond Graph Approach, Springer Verlag, Series: Advances in Industrial Control, 490 pages. ISBN: 978-1-84800-158-9, Berlin, 2008.

[19] B. Ould Bouamama, K. Medjaher, A.K. Samantary and M. Staroswiecki, “Supervision of an industrial steam generator. Part I: Bond graph Modelling,” Control Engineering Practice, vol. 14, no. 1, pp 71-83, 2006.

[20] B. Ould Bouamama., M. Staroswiecki et Litwak R, Surveillance d'un générateur de vapeur, chap. 6. pp. 168-199, Collection IC2 Edition Hermes, Paris 2001.

[21] S. Benmoussa, R. Merzouki and B. Ould-Bouamama, Chapter 6: Bond Graph Model- Based Fault detection and Isolation: Application to Intelligent Autonomous Vehicle ebook-Mechatronic & Innovative Applications, Bentham Science. ISBN: 9781608054404, pp. 111-126, 2012.

[22] A. Aitouche and B. Ould Bouamama, “Detecting and Isolating Actuators Faults of Steam Boiler,” International Journal of Sciences and Techniques of Automatic control & computer engineering (IJ-STA), vol. 2, no. 2, pp. 764−775, 2008.

[23] B. Ould Bouamama, A.K. Samantary, K. Medjaher, M. Staroswiecki and G. Dauphin- Tanguy, “Model builder using Functional and bond graph tools for FDI design,” Control Engineering Practice, vol. 13, no. 7, pp. 875-89, 2005.

[24] B. Ould Bouamama, K. Medjaher, M. Bayart, A.K. Samantaray and B. Conrard, “FDI of Smart actuators using Bond Graphs and external models,” Control Engineering Practice, vol. 13, no. 2, pp. 159-175, 2005.

[25] R. Loureiro, S. Benmoussa, Y. Touati, R. Merzouki, B. Ould Bouamama, “Integration of fault diagnosis and fault tolerant control for healthy monitoring of class of MIMO Intelligent Autonomous vehicles,” IEEE Transaction on vehicular Technology, vol. 63, no. 1, pp. 30-39, 2014.

[26] Aitouche and B. Ould Bouamama, “Sensor location with respect to fault tolerance Properties,” International Journal of Automation and Control Inderscience Publishers, vol.4, no. 3, pp. 298-316, 2010.

[27] W. El Osta, C. Sueur et B. Ould Bouamama, “Monitorability indexes for fault tolerance analysis using a bond graph approach,” 6th IFAC, SAFEPROCESS, pp. 1555-1560, Beijing, 2006.

[28] M. Lienhardt, A. Aitouche et B. Ould Bouamama, “Fault Tolerant Estimation for Multisensor Non linear Systems,” International Mediterranean Modeling Multiconference (I3M), Proceedings edited by A. G Bruzzone et al. vol. 2 pp. 177-181, Genova 2004.

[29] T. B. Reiter, “Human and computers roles in supervisory control and telerobotics,” Musing about function, language and hierarchy. In L.P.Goodstein, H.B. Andersen, and S.E. Olsen, (Eds.) Task, errors and mental models.

Taylor & Francis. London. UK, 1988.

[30] J. Rasmussen. “Skills, rules, and knowledge; Signals, signs, and symbols and other distinction in Human Performance Models,” In: IEEE transactions on systems man and sybernetics, vol. SMC-13, no. 3, pp. 257, 1983.

[31] M. Staroswieck et M. Bayart, “Models and languages for the interoperability of smart instruments,”Automatica, vol. 32, no. 6, pp. 859-873, 1996.

[32] B. Ould Bouamama,“Fault detection and isolation of smart actuators using Bond graph and external models,” In Control Engineering Practice, vol. 13, no. 2, pp. 159-175, 2004.

BIOGRAPHIES OF AUTHORS Abderrahmene Sallami was born in Tunis; Tunisia 1970. He obtained the master diploma and doctoral diploma in Electro-technical from National Engineering School of Tunis (ENIT) in Tunisia. He is a member of the Research Unit of Renewable Energies at The High Institute of Technologic Studies of Bizerte. His field of interest concerns fuel cells, photovoltaic power, treatment water, supervision using Bond Graph modeling.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 313 – 325

Int J Adv Appl Sci

ISSN: 2252-8814



325

Dhia Mzoughi was born in 1984 in Tunisia. He received the diploma of master's degree then the PhD from the Faculty of Sciences of Tunis in 2011 and 2015 respectively, all in electronics. He is currently an Assistant and a researcher at the Analysis, Design and Control Systems Laboratory at the National School of Engineering of Tunis (ENIT). His current research interests include control and modeling of fuel cell, renewable energy conversion, power electronics and supervision using Bond Graph modeling.

Hatem Allagui was born Tunisia. Is an Assistant professor at the University of Sciences of Tunis. Now, he is a searcher at the Analysis, Design and Control Systems Laboratory. He obtained the diploma of engineer then the PHD in electrical engineering at 2003 from the National school of engineers of Tunis. His current interest includes embedded electronics, power electronics and renewable energy.

Abdelkader Mami was born in Tunisia, he is a Professor in Faculty of Sciences of Tunis (FST).

He was received his Dissertation H.D.R (Enabling To Direct of Research) from the University of Lille (France) 2003, he is a member of Advice Scientific in Faculty of Science of Tunis (Tunisia), he is a President of commutated thesis of electronics in the Faculty of sciences of Tunis. He is a person in charge for the research group of Analysis, Design and Control Systems Laboratory at the National School of Engineering of Tunis (ENIT) and in many other fields.

Efficiency of bond graph and external model integration for alarm processing of … (Abderrahmene Sallami)

International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 326~332 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp326-332



326

Powerful processing to three-dimensional facial recognition using triple information Mohammad Karimi Moridani1, Ahad Karimi Moridani2, Mahin Gholipour3 1,3Department

of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Iran 2Technologies de L'information Collège CDI, Canada

Article Info

ABSTRACT

Article history:

Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face.

The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.

Received Apr 2, 2020 Revised Jun 11, 2020 Accepted Jul 9, 2020 Keywords: 3D image Facial information Information processing, Mean and gaussian curvature SVM

This is an open access article under the CC BY-SA license.

Corresponding Author: Mohammad Karimi Moridani, Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran Province, Tehran, Danesh Rd, Iran.

Email: karimi.m@iautmu.ac.ir

1.

INTRODUCTION Face detection has great importance for many as groups. The crucial applications of Face Detection in areas such as human-computer interface, human authentication, criminals face detection, face monitoring systems is undeniable. In all methods proposed to determine the position of specific points in threedimensional images, the nose is regarded as a typical point, and the rest of the points can be located.

To determine the location of the nose, several methods were proposed. In some methods, the nearest position of the camera is regarded as the nose, and then the other points are extracted based on the nose position.

This assumption is true in the case of no rotation around the x and y-axis; in fact, the image must be on the front side.

Xu et al. [1] introduced a feature extraction hierarchical plan to identify the positions of the nose tip and nose ridge.

They presented an effective energy idea to show the local distribution of neighboring points and detect the candidate nose tips.

In the end, a support vector machine (SVM) classifier was used to choose the right nose tips.

Dibeklio˘glu et al. [2, 3] displayed strategies for recognizing facial information on 3D facial datasets to empower posture amendment under important posture varieties.

Journal homepage: http://ijaas.iaescore.com

Int J Adv Appl Sci

ISSN: 2252-8814



327

They presented a statistical strategy to distinguish facial elements using the gradient of the depth map based on preparing a model of local elements. The technique was tried against the FRGC v1 and the Bosphorus databases; however, information with posture varieties was not taken into the investigation.

They likewise presented a nose tip localization and segmentation technique utilizing curvaturebased heuristic investigation. Also, even though the Bosphorus database utilized comprises of 3,396 facial scans, they are acquired from 81 subjects. At last, no correct localization distance error results were displayed.

In other research, Romero-Huertas et al. [4] introduced a graph matching method to determine the places of the nose tip and internal eye corners. They presented the distance to local plane idea to portray the local distribution of neighboring points and identify convex and concave regions of the face. At long last, after the graph matching method has wiped out false landmarks, according to the minimum Mahalanobis distance, the best combination of landmarks points is chosen to the trained landmark diagram model.

The technique was tried against FRGC v1 and FRGC v2 databases, including 509 scans and 3271 scans, respectively. They detailed a considerable rate of 90% with thresholds for the nose tip at 15 mm, and for the internal eye corners at 12 mm.

At last, Perakis et al. [5, 6] displayed techniques for distinguishing important facial points such as eye inner and outer corners, mouth corners, and nose and chin tips) based on 2.5D scans. Neighborhood shape and curvature processing using shape index, expulsion maps, and turn pictures were utilized to find candidate landmark points. These are determined and marked by coordinating them with a statistical facial landmark model [7, 8].

The goal of this paper is to find out an efficient face registration method based on triple.Then we will introduce the problems related to the existing systems in face recognition and possible research ways that help to solve these issues. Three dimensional methods could provide better robustness to create diversity than 2D-based methods. Automatic face recognition detection has become one of the most important research topics in image processing due to its application in human-robot interactions, pain detection, lie detection, and other psychological and medical applications.

2.

MATERIAL AND METHOD In this method, it is assumed that a true display of a three-dimensional image as a piece of the image for each location (j, i) coordinates (X, Y, Z) the three-dimensional scene. Some of the images received from the device illustrate the data on a form of polygon models, usually called the triangular model. In this case, the range of the image can be detected using the Z-buffer algorithm [1]. As a result, the resulting image may include any number of faces; in that case, it depends on the need and also the imaging device. To avoid the less computational problem, we first seek facial features such as eyes and nose. As a result, this is the first step in image segmentation in related areas of the face features.

Currently, our goal is to distinguish the face triangle on real faces. This method is registered for the position and standard orientation of the face and reduction of the variability of the depth of the face.

The triangle distance of the nose and eyes in the image is also calculated.

2.1. Nose and eyes identification The HK segmentation method is used to find the nose and the inner corners of the eyes.

For a surface, with a curvature H mean and K Gaussian curvature, the facial expression can be identified.

In this case, the nose is the maximum point, and the corners of the eyes are the minimum. Based on the average curvature of the face, and because of the leading position and hilly of the nose, it has the highest curvature in the surrounding area, so regarding the face, the points with the maximum curvature are searched.

For this purpose, the mean surface curvature is calculated. Afterward, the areas with a greater curvature specified and the area with the greatest curvature will be the tip of the nose. If the curvature of an area is greater, therefore the total amount of curvature in the area will be more obvious. In addition to that, because of the noise, Curvature enhances in one or several pixels, but the high curvature of the searched area is curved, the filter reduces the noise and averages the effects of preventing the wrong location by calculating the mean [9]. The image of the mean curvature, before and after the filtering is shown in Figure 1.

This method determines the position of the nose under any desired angle around the x, y, and z.

The only limitation is more likely if more than half of the nose is hidden around the y-axis. This method utilizes the mean curvature of the face and does not take advantage of the educational and classified data.

For this reason, this method has a higher computational speed than the methods that find the nose from different angles.

Powerful processing to three-dimensional facial recognition using … (Mohammad Karimi Moridani)

328



ISSN: 2252-8814

Figure 1. The front part of the face (top right), the image of average curvature (top left), the front part of the face with more light intensity (bottom left), the image of the average curvature after filtering (bottom right)

2.2. Recognition in image rotation The computational cost of the use of the rotating image is high. Initially, a series of areas that are likely to locate the nose and eyes are specified, then to locate the exact area, the rotating image is extracted, and using SVM (Support Vector Machine) classification, the exact located is specified [10, 11]. This method needs primary data and a neural network. To determine the location of the nose, the angles around the y-axis is quantized, and then the images rotate around the y-axis, then the nearest point to the camera is considered as a candidate. After a full rotation around the y-axis, the nose is diagnosed. This method can identify the nose position if the rotation is around the y-axis, and this method is not cost-effective if the rotation happens to the other angles.

2.3. Three-dimensional face rotation Three rotational matrices or their multiplied structure can be used to rotate three-dimensional images around the axes of rotation [12-14]. If we assume that alpha is rotation around axis X, Beta is rotation around axis Y, and Lambda is rotation around the axis of Z, then the formula around each axis will be as (1) to (3). In formula (4) the result is the by multiplying form of the three matrices: 1 0 0 𝑅1 (𝛼) = [0 𝐶𝑜𝑠𝛼 −𝑆𝑖𝑛𝛼 ] 0 𝑆𝑖𝑛𝛼 𝐶𝑜𝑠𝛼

(1)

𝐶𝑜𝑠𝛽 0 𝑆𝑖𝑛𝛽 0 ] 𝑅2 (𝛽) = [ 0 1 −𝑆𝑖𝑛𝛽 𝑆𝑖𝑛𝛼 𝐶𝑜𝑠𝛽

(2)

𝐶𝑜𝑠𝛾 −𝑆𝑖𝑛𝛾 0 𝑅3 (𝛾) = [ 𝑆𝑖𝑛𝛾 1 0] 0 1 0

(3)

𝑅 = 𝑅1 ∗ 𝑅2 ∗ 𝑅3 = 𝐶𝑜𝑠𝛽. 𝐶𝑜𝑠𝛾 −𝐶𝑜𝑠𝛽. 𝑆𝑖𝑛𝛾 𝑆𝑖𝑛𝛽 [ 𝑆𝑖𝑛𝛼. 𝑆𝑖𝑛𝛽. 𝐶𝑜𝑠𝛾 + 𝐶𝑜𝑠𝛼. 𝑆𝑖𝑛𝛾 −𝑆𝑖𝑛𝛼. 𝑆𝑖𝑛𝛽. 𝑆𝑖𝑛𝛾 + 𝐶𝑜𝑠𝛼. 𝑆𝑖𝑛𝛾 −𝑆𝑖𝑛𝛼. 𝐶𝑜𝑠𝛽 ] −𝐶𝑜𝑠𝛼. 𝑆𝑖𝑛𝛽. 𝐶𝑜𝑠𝛾 + 𝑆𝑖𝑛𝛼. 𝑆𝑖𝑛𝛾 𝐶𝑜𝑠𝛼. 𝑆𝑖𝑛𝛽. 𝑆𝑖𝑛𝛾 + 𝑆𝑖𝑛𝛼. 𝐶𝑜𝑠𝛾 𝐶𝑜𝑠𝛼. 𝐶𝑜𝑠𝛾

(4)

As a result, vectors on each spin is obtained with the coordination of the rotated image.

The intensity is added to use this matrix, three-dimensional data, and two-dimensional image rotation. So, the image rotation coordination is found. The data matrix is changed as formula (5), and the intensity was added to the last line of image data.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 326 – 332

Int J Adv Appl Sci

ISSN: 2252-8814

𝑥1 𝑥2 𝑥3 𝑥4 𝑥5 . . . . 𝑦1 𝑦2 𝑦3 𝑦4 𝑦5 . . . . 𝐷 = [ 𝑧 𝑧 𝑧 𝑧 𝑧 . . . .]

1 2 3 4 5 𝐼1 𝐼2 𝐼3 𝐼4 𝐼5 . . . .



329

(5)

The important point here is that two and three-dimensional images of the same size, and both must be accurate with a complete picture of the interaction compliance. The database used; the two-dimensional images are larger than the three-dimensional images. Therefore, first two-dimensional images are resized to be the same size with dimensions of three-dimensional images. After that, a common point in both images is marked, and two images are characterized by y and x overlap, then the data is available for each coordination [15, 16]. Due to the change of the data matrix, a rotation matrix also needs to be changed.

Since the intensity data does not affect the pixel location, the rotation matrix will be as formula (6).

Where the available zero and one data are added to the rotation matrix, so the light intensity data is transmitted without altering.

0 0 𝑅 [ ] 0 0 0 01

(6)

Then the collected data in a two-dimensional matrix are transformed into a two-dimensional rotated image [17].

2.4. Face detection The use of information procedure has several advantages. First of all, the first data is for the depth, not the intensity of the light, so independent of the severity of the light or the light radiation angle or the face size, the obtained image is fixed. Also, these data do not use the reflected light from the face. Therefore, it is not dependent on the skin color changes affected by the makeup or sunlight and sunburn. Generally, the three-dimensional data can be moved and rotated in the desired angle in three-dimensional space; accordingly, the formula is extracted under the desired angle. Therefore, if the image is not at the desired angle, it can be rotated to calculate the image at the desired angle [18-20].

The need for three-dimensional imaging cameras, which are more expensive than two-dimensional imaging cameras, is one of the disadvantages of this method. Facial hair is one of the cases which makes the extraction of depth data difficult [20]. Due to the detection of faces by the trinity of eyes and nose, the system can get along with problems such as hiding the ear, hair, eyebrows, etc. (Figure 2).

Figure 2. Face detection using nose and eyes

The complete procedure can be extracted through a panoramic camera or multiple photos in different directions.

Powerful processing to three-dimensional facial recognition using … (Mohammad Karimi Moridani)

330



ISSN: 2252-8814

2.5. Experimental testing The proposed method implemented on the Bosphorus database on 150 faces shows that this method is 99.7% accurate in the diagnosis of the position of the nose, 99.3% correct diagnosis of the position of the eyes, and 96.7% accurate diagnosis of eye position. In Table 1, the results determine the location of the eyes, nose, and internal parts of the eyes, which were shown in the database.

Table 1. The number of nose and eyes positions which were detected Viewing Angle

143 143

Left Eye Detection 141 143

Right Eye Detection 139 139

143

143

141

143

143

141

143 141 143

142 141 143

140 140 141

142

142

Nose Detection

Front Photo Rotate 25 degrees around Y Low rotation around the z-axis High rotation around the z-axis Smile Picture Open Mouth The rotation around the x-axis (above) The rotation around the x-axis (down)

3.

RESULTS AND DISCUSSION The proposed algorithm is implemented on 14 pictures of 150 persons is shown in Table 2.

In Figure 3 the accuracy graph is plotted for different errors.

Table 2. Estimation accuracy of angle for various errors The error 10 ° error.

6 ° Error 3 ° Error

z-Axis 21 98% 94.88% 77.46%

y-Axis 99.52% 98.81% 95.00%

x-Axis 98.81% 92.33% 67.24%

Figure 3. Accuracy diagram for different errors

The running time of this Algorithm, regardless of the time needed to determine the location of the nose, is less than 800 milliseconds. Moreover, this method requires no data for training. Because of the hierarchical structure in case of the wrong estimation of the angles, the wrong estimation of the other angles is also possible. Due to the data mapping of the Z = 0, the existence of an error in the calculation of Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 326 – 332

Int J Adv Appl Sci



ISSN: 2252-8814

331

the angle in the direction of the z-axis is possible. If we can calculate parameters using the ellipse in threedimensional space, then the accuracy can be increased. Also, if the angle along the x-axis and y can be determined from the original image along with the angle determination, then the Speed and error of the method will be decreased.

One of the most important capabilities of the human visual system is the ability to recognize the face, which plays an important role in the social life of each person. Our relationship with other people is based on our ability to identify them. One of the most prominent aspects of the human face recognition system is its high accuracy and reliability, but all human beings do face recognition easily and without much effort, while this is not an easy task for computers. The main purpose of this study is to study the effect of selecting the appropriate features of three-dimensional images for face recognition. The results show the success of this method in identifying faces with different characteristics. Different approaches so far are summarized in Table. 3.

Table 3. Comparison of classification accuracy of different methods Name

3D face database

Blanz et al [21] Dibeklioglu et al [22] Mahmood et al [23] Berretti et al [24] Perakis et al [25] Ding et al [26] Proposed method

FRGC Bosphorus GavabDB UND FRGC v2.0 GavabDB UND LFW Bosphorus

Maximum Accuracy 92% 79.41% 90% 82.1% – 92.95% 99.52%

Due to the sensitive nature of face detection, high precision is of the utmost importance.

Obtained results show that the open mouth increases the possibility of the loss of the nose. Considering that this method is not only to identify the nose and eyes identification are also considered, so this weakness is covered, but in the situation in which both eyes are covered or somehow that part of the image is lost or if the mouth is open; the identification will be less than when the mouth is closed.

4.

CONCLUSION Human face recognition by machine through 3D images has become an active research phase in image processing communities, pattern recognition, neural networks, and computer vision.

The face plays an essential role in identifying people and showing their feelings at the community level. The human ability to recognize faces is remarkable. We can recognize thousands of memorable faces in our lifetime and, at a glance, recognize familiar faces even after years of separation. Face recognition has become an important issue in applications such as security systems, credit card control and crime identification.

In this article, a new method to face recognition automatically using 3D face images was presented.

The geometric features of the face were used to identify faces with the help of neural networks. The results showed. The results of using the proposed method in this paper with the help of Bosphorus database showed the highest percentage of accuracy compared to previous methods.

ACKNOWLEDGMENT I would like to express my deepest appreciation to Professor Bulent Esnikor from Boğaziçi University for providing me with significant data that greatly assisted the research.

REFERENCES [1] [2] [3] [4]

Xu C., Tan T., and Wang Y., Quan L., “Combining local features for robust nose location in 3D facial data,” Pattern Recognition Letters, vol. 27, no. 13, pp. 62-73, 2006.

Dibeklioglu H., Part-based 3D face recognition under pose and expression variations, Master's thesis, Bo_gazi_ci University, 2008.

Dibeklioglu H., Salah A., Akarun L., “3D facial landmarking under expression, pose, and occlusion variations,” In Proc. 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 1- 6, 2008.

Romero-Huertas M., Pears N., “3D facial landmark localization by matching simple descriptors,” In Proc. 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, 2008.

Powerful processing to three-dimensional facial recognition using … (Mohammad Karimi Moridani)

332 [5]

[6] [7] [8] [9] [10]

[11]

[12] [13]

[14] [15]

[16] [17] [18] [19]

[20] [21] [22] [23] [24] [25] [26]



ISSN: 2252-8814

Perakis P., Passalis G., Theoharis T., Toderici G., Kakadiaris I., “Partial matching of interpose 3D facial data for face recognition,” In Proc. 3rd IEEE International Conference on Biometrics:Theory, Applications and Systems, pp. 439-446, 2009.

Perakis P., Theoharis T., Passalis G., Kakadiaris I., “Automatic 3D facial region retrieval from multi-pose facial datasets, In Proc. Eurographics Workshop on 3D Object Retrieval, pp. 37-44, 2009.

Xie W, Shen L, Yang M, Lai Z., “Active AU based patch weighting for facial expression recognition,” Sensors (Basel), vol. 17,pp. 275, 2017.

S. Berretti, A. Del Bimbo, P. Pala, “Sparse matching of salient facial curves for recognition of 3D faces with missing parts,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 2, pp.374-389, 2013.

Andrew J.Logan, Gael E.Gordon, GunterLoffler, “Contributions of individual face features to face discrimination,” Vision Research, vol. 137, pp. 29-39, 2017.

S. Ravi, S. Wilson, “Face detection with facial features and gender classification based on support vector machine,” 2010 IEEE International Conference on Computational Intelligence and computing Research, ISBN: 97881 8371 3627, 2010 T. Verma and R. K. Sahu, “PCA-LDA based face recognition system & results comparison by various classification techniques,” 2013 International Conference on Green High Performance Computing (ICGHPC), Nagercoil, pp. 1-7, 2013.

Zhou, S., Xiao, S. “3D face recognition: a survey.” Hum. Cent. Comput. Inf. Sci. vol. 8, pp. 35, 2018.

Borade S.N., Deshmukh R.R., Shrishrimal P., “Effect of distance measures on the performance of face recognition using principal component analysis,” In: Berretti S., Thampi S., Srivastava P. (eds) “Intelligent systems technologies and applications,” Advances in Intelligent Systems and Computing, vol. 384, 2016.

S.M.S. Islam, R. Davies, M. Bennamoun, R. "Owens and Ajmal Mian, “Multibiometric human recognition using 3D ear and face features,” Pattern Recognition, vol. 46, no. 3, pp. 613-627, 2013.

X.Zhao, W.Zhang, G.Evangelopoulos, D.Huang, S.Shah, Y.Wang, I.Kakadiaris and L. Chen, “Benchmarking asymmetric 3D-2D face recognition systems,” Proc. 3D Face Biometrics Workshop, in conjuction with IEEE Int’l Conf. on Automatic Face and Gesture Recognition (FG), 2013.

Yueming Wang, Gang Pan, Jianzhuan Liu, “A deformation model to reduce the effect of expressions in 3D face recognition,” Visual Computer, vol. 27, no. 5, pp. 333-345, 2011.

A. Watt, M. Watt, Advanced Animation, and Rendering Techniques: Theory and Practice, Addison-Wesley, Reading, MA, 1992.

A. Moreno, A. Sanchez, J. Velez, F. Diaz, “Face recognition using 3D surface extracted descriptors,” Proceedings of the Irish Machine Vision and Image Processing, 2004.

Naeem Iqbal Ratyal, Imtiaz Ahmad Taj, Muhammad Sajid, Nouman Ali, Anzar Mahmood and Sohail Razzaq, “Three-dimensional face recognition using variance-based registration and subject-specific descriptors,” International Journal of Advanced Robotic Systems, pp. 1-16, 2019.

Miller, P., and Lyle, J. The effect of distance measures on the recognition rates of PCA and LDA based facial recognition Tech. rep., Clemson University, 2008.

Blanz V, Scherbaum K, Seidel HP, “Fitting a morphable model to 3D scans of faces,” In: Computer vision, pp. 1-8, 2007.

Dibeklioglu H, Salah AA, Akarun L., “3D facial landmarking under expression, pose and occlusion variations,” In: Biometrics theory, applications and systems, pp. 1-6, 2008.

Mahmood SA, Ghani RF, Kerim AA., “3D face recognition using pose invariant nose region detector,” In: Computer science and electronic engineering conference, 2014.

Berretti S, Del Bimbo PPA., “Sparse matching of salient facial curves for recognition of 3D faces with missing parts,” Forensics Secu, vol. 8, pp. 374-389, 2013.

Perakis P, Passalis G, Theoharis T, Toderici G, Kakadiaris IA., “Partial matching of interpose 3D facial data for face recognition,” In: Biometrics: theory, applications, and systems, pp. 439-446, 2009.

Hua WG., “Implicit elastic matching with random projections for pose-variant face recognition,” In: Comput. Vis. Pattern Recognit, pp. 1502-1509, 2009.

Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 326 – 332

Institute of Advanced Engineering and Science Indonesia

: D2, Griya Ngoto Asri, Bangunharjo, Sewon, Yogyakarta 55187, Indonesia

Malaysia

: 51 Jalan TU 17, Taman Tasik Utama, 75450 Malacca, Malaysia

COPYRIGHT TRANSFER FORM (Please compile this form, sign and send by e-mail) Please complete and sign this form and send it back to us with the final version of your manuscript. It is required to obtain a written confirmation from authors in order to acquire copyrights for papers published in the International Journal of Advances in Applied

Sciences (IJAAS) Full Name and Title Organisation Address and postal code City Country Telephone/Fax E-mail Paper Title Authors

Copyright Transfer Statement The copyright to this article is transferred to Institute of Advanced Engineering and Science (IAES) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to IAES. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.

We declare that: 1. This paper has not been published in the same form elsewhere.

2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.

3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.

Furthermore, I/We hereby transfer the unlimited rights of publication of the above-mentioned paper in whole to IAES. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.

The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.

Retained Rights/Terms and Conditions 1.

2.

3.

Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.

Authors may reproduce or authorize others to reproduce the Work or derivative works for the author’s personal use or for company use, provided that the source and the IAES copyright notice are indicated, the copies are not used in any way that implies IAES endorsement of a product or service of any employer, and the copies themselves are not offered for sale.

Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.

Yours Sincerely,

Corresponding Author‘s Full Name and Signature Date: ……./……./…………

International Journal of Advances in Applied Sciences (IJAAS) email: ijaas@iaescore.com, iaes.editor@gmail.com

Guide for Authors International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of- the art research activities in applied science, engineering and technology areas, which cover topics including: industrial engineering, materials & manufacturing; mechanical, mechatronics & civil engineering; food, chemical & agricultural engineering; telecommunications, computer science, instrumentation, control, electrical & electronic engineering; and acoustic & music engineering.

Papers are invited from anywhere in the world, and so authors are asked to ensure that sufficient context is provided for all readers to appreciate their contribution.

The Types of papers The Types of papers we publish. The types of papers that may be considered for inclusion are: 1) Original research; 2) Short communications and; 3) Review papers, which include meta-analysis and systematic review

How to submit your manuscript All manuscripts should be submitted online at http://ijaas.iaescore.com

General Guidelines 1) Use the IJAAS guide http://iaescore.com/gfa/ijaas.docx) as template.

2) Ensure that each new paragraph is clearly indicated. Present tables and figure legends on separate pages at the end of the manuscript.

3) Number all pages consecutively. Manuscripts should also be spellchecked by the facility available in most good word-processing packages.

4) Extensive use of italics and emboldening within the text should be avoided.

5) Papers should be clear, precise and logical and should not normally exceed 3,000 words.

6) The Abstract should be informative and completely self-explanatory, provide a clear statement of the problem, the proposed approach or solution, and point out major findings and conclusions. The Abstract should be 150 to 250 words in length. The abstract should be written in the past tense.

7) The keyword list provides the opportunity to add keywords, used by the indexing and abstracting services, in addition to those already present in the title. Judicious use of keywords may increase the ease with which interested parties can locate our article.

8) The introduction should provide a clear background, a clear statement of the problem, the relevant literature on the subject, the proposed approach or solution, and the new value of research which it is innovation. It should be understandable to colleagues from a broad range of scientific disciplines.

9) Explaining research chronological, including research design and research procedure. The description of the course of research should be supported references, so the explanation can be accepted scientifically.

10) Tables and Figures are presented center.

11) In the results and discussion section should be explained the results and at the same time is given the comprehensive discussion.

12) A good conclusion should provide a statement that what is expected, as stated in the "Introduction" section can ultimately result in "Results and Discussion" section, so there is compatibility. Moreover, it can also be added the prospect of the development of research results and application prospects of further studies into the next (based on the results and discussion).

13) References should be cited in text by numbering system (in IEEE style), [1], [2] and so on. Only references cited in text should be listed at the end of the paper.

One author should be designated as corresponding author and provide the following information: • E-mail address • Full postal address • Telephone and fax numbers Please note that any papers which fail to meet our requirements will be returned to the author for amendment. Only papers which are submitted in the correct style will be considered by the Editors.

International Journal of Advances in Applied Sciences (IJAAS) Institute of Advanced Engineering and Science (IAES) e-mail: ijaas@iaesjournal.com

IJAAS Journal Order Form Volume

Number

Amount

Price (Rp)

Price (USD)

Total

Name

:

Company

:

Address

:

City / State

:

Zip

:

Telephone/Fax

:

email

: ........................, ..........................

Signature: ..................................

Order form for subscription should be sent to the editorial office by fax or email

Payment by Bank Transfer Bank Account name (please be exact)/Beneficiary: LINA HANDAYANI Bank Name: CIMB NIAGA Bank Branch Office: Kusumanegara City: Yogyakarta Country :Indonesia Bank Account # : 5080104447117 Bank Swift : BNIAIDJAXXX >>> Please find the appropriate price in the price list on next page >>>

The price list for domestic and foreign subscribers

Volume

Number

Year

1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

2012 2012 2012 2012 2013 2013 2013 2013 2014 2014 2014 2014 2015 2015 2015 2015 2016 2016 2016 2016 2017 2017 2017 2017 2018 2018 2018 2018 2019 2019 2019 2019 2020 2020 2020 2020

Price (IDR) for domestic subscribers 290.000,00 290.000,00 290.000,00 290.000,00 290.000,00 290.000,00 290.000,00 290.000,00 319.000,00 319.000,00 319.000,00 319.000,00 319.000,00 319.000,00 319.000,00 319.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00 349.000,00

Price (USD) for foreign subscribers 36 36 36 36 36 36 36 36 40 40 40 40 40 40 40 40 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44 44

The price included the printing, handling, packaging and postal delivery fees of the hardcopy to the address of the authors or subscribers (with Registered Mail). For foreign subscribers, an additional fee is charged if you would your order is mailed via Express Mail Service (EMS): - $25 for ASIA Continent - $35 for AUSTRALIA Continent - $35 for AFRICA Continent - $39 for AMERICA Continent - $39 for EUROPE Continent (No additional fee for delivering your order by Registered Mail).