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 areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.
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IJAAS
International Journal of
Advances in Applied Sciences
The effect of e-cigarette and a conventional cigarette to the alveolus on Wistar male rats Budian Nurpangestu, Yusrotun Kharimah, Fita Linggasati, Moch Bahrudin
251-256
Evaluation of shale volume and effective porosity using larionov and archie equations from wire-line logs, Niger delta Nigeria Mfoniso U. Aka, Johnson Cletus Ibuot, Francisca N. Okeke
257-263
Survey of part-of-speech tagger for mixed-code Indian and foreign language used in social media Bhushan Ashokrao Nikam
264-268
Real time simulation of sensorless control based on back-EMF of PMSM on RT-Lab/ARTEMIS real-time digital simulator Abdelhakim Idir, A. Ahriche, K. Khettab, Y. Bensafia, M. Kidouche
269-278
Solving optimal reactive power problem by improved variable mesh optimization algorithm Kanagasabai Lenin
279-284
Real power loss reduction by dolphin swarm algorithm Kanagasabai Lenin
285-289
Optimization of transmission signal by artificial intelligent Hassan Farahan Rashag, Mohammed H. Ali
290-292
Multi-objective wind farm layout optimization using evolutionary computations Chandra Shekar, M. R Shivakumar
293-306
Responsibility of the contents rests upon the authors and not upon the publisher or editors.
IJAAS
Vol. 8
No. 4
pp. 251-306
December 2019
ISSN 2252-8814
International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 4, December 2019, pp. 251~256 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i4.pp251-256
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The effect of e-cigarette and a conventional cigarette to the alveolus on Wistar male rats Budian Nurpangestu1, Yusrotun Kharimah2, Fita Linggasati3, Mochamad Bahrudin4 1,2,3 4
Faculty of Medicine, University of Muhammadiyah Malang, Indonesia Neurology Division, University of Muhammadiyah Malang, Indonesia
Article Info
ABSTRACT
Article history:
Smoking can cause a variety of diseases one of them on pulmonary organs, e-cigarettes are also considered to be safer than conventional cigarettes and the number of smokers in Indonesia is increasing every year. The purpose of this study to determine differences in pulmonary alveolar damage in male rats (Rattus norvegicus Wistar strain) on electric cigarette smoke exposure and conventional cigarettes. This research is true experimental with post-test only control group design. Male rats were used as many as 25 rats were divided into 5 groups, namely the group P0 as a negative control group; P1 group was exposed to clove cigarettes; P2 group is exposed to a filter cigarette; P3 group was exposed to e-cigarette 0mg; P4 group exposed the ecigarette 3mg. Samples were treated for 30 days. Data were analyzed using Kruskal Wallis with p < 0.05. The results showed that there were significant differences in alveolar damage in the fifth group (p = 0.003). With Mann Whitney shows that groups P1, P2, P3, and P4 there are no significant differences. The conclusion of this study is there is no difference in alveolar damage on Wistar male rats by e-cigarettes and conventional cigarettes exposure.
Received Jul 8, 2019 Revised Sep 7, 2019 Accepted Oct 6, 2019 Keywords: Alveolus Conventional cigarette E-cigarette
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Budian Nurpangestu, Faculty of Medicine, University of Muhammadiyah Malang, Malang 65145, Indonesia. Email: budian_np@yahoo.com
1.
INTRODUCTION Smoking can cause a variety of health problems both on active or passive smokers. 5 According to the WHO cause of death in the world with one of the risk factors of smoking. According to the 3rd edition of The Tobacco, Atlas ASEAN is a region with 10% of the world's smokers and 20% of the causes of global mortality due to tobacco and Indonesia percentage of the population who smoke amounted to 46.16% [1]. The mean population is aged ≥ 10 years of smoking Indonesia 12.3 stems per day (the equivalent of one pack) with the most age distribution of active daily smokers at age 30-34 years at 33.4% with the percentage of men more compared female smokers (62.9% versus 4.8%) [2]. Cigarettes produced more than 4000 materials from burning and hundreds of them are addictive. In any suction of smoke, there are 1014 free radicals and can be maintained in a relatively long time >10 minutes [3]. In addition to nicotine in cigarettes are also found tar compounds, gases CO (carbon monoxide), BAP (benzopyrene), TSNA (tobacco-specific nitrosamines), pesticide residues, and other substances that are not less harmful when compared with nicotine [4]. Currently circulating e-cigarette that was first introduced in 2003 [5]. The main content of the e-cigarette glycerin, propylene glycol, water and nicotine, some other constituents of carbon monoxide (CO), carbonyls, phenolics, volatile organic compounds (volatiles), metals, tobacco-specific nitrosamines (TSNAs), polyaromatic amines (PaaS), and polyaromatic hydrocarbons (PAHs) [6]. E-cigarette users in Indonesia in 2011 reached 0.3% and in 2015 reached 2.5% of the total Journal homepage: http://iaescore.com/online/index.php/IJAAS
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population [7] with a spread of ages 12-24 years at 14.4% and at the age of 25-44 12.4% and the number of users in the college of 29.4% [8]. At the initial appearance of e-cigarette is claimed to be safe for health because nicotine and toxic substances in an e-cigarette are lower than conventional cigarettes [9]. Seeing the problems that exist today based on data Riskesdas 2018, increasing the number of smokers in Indonesia every year and certainly will be in line with the increase in passive smokers in Indonesia, according to Department of Health & Human Services, State Government of Victoria in 2018 states that Smoke from secondhand smoke contains carbon monoxide three times as many, 10 to 30 times more nitrosamines, and 15 to 300 times as much ammonia. Passive smokers have a risk five times more susceptible to the disease affected by active smokers, more than 40.3 million children in Indonesia live with smokers that are exposed to the smoke. They are at risk for bronchitis, pneumonia, asthma, etc. Passive smokers also cause a decrease in the level of health in adulthood. Active and passive smokers at risk for lung cancer and other cancers [10]. Electronic Cigarette Association (ECA) had not involved returning the electronic cigarette to quit smoking program because it turns out there are harmful substances in it the same as conventional. Based on a study of toxic substances such as arsenic, cadmium, tobacco-specific nitrosamines (TSNA), ammonia, carbon monoxide, aromatic amines, volatile organic compounds (VOC), nicotine, and polyaromatic hydrocarbons where all the material is not good for health [11]. In previous similar studies have been conducted observations about the lung damage that affect by e-cigarette exposure and as a result there is a histology lung damage, but these studies need to be developed because the research only 2 weeks, so the results less than the maximum and cigarette use were less variable [12] and based on other studies of pulmonology faculty of medicine university of Indonesia regarding the effects of electronic cigarette turns the electronic cigarette has harmful effects on the lungs just like a conventional cigarette. However, further research is still needed on the difference between conventional cigarettes and electronic cigarettes in long-term use. Because there are a lot of clinical data showing the effect of the use of electronic cigarettes in the long term [13], Seeing mash a little research on conventional cigarette comparison with the e-cigarette therefore in this study compared to the level of damage to cells in the alveoli of exposure conventional cigarettes and e-cigarette so hopefully with this study can add to the study of research and to develop knowledge in the medicine and science, in addition to the existence of this research could change society's view of the electric cigarette is still considered safe and become a lifestyle, especially among teenagers. The purpose of this study was to prove the existence of differences in the level of damage to cells in the alveolar white male rats (Rattus norvegicus Wistar strain) by exposure to e-cigarettes and conventional cigarettes. Benefits expected of the program is to give knowledge to the community and medical personnel on the health effects caused by exposure to e-cigarettes and conventional cigarettes smokes, especially in the lungs, the community hopes to understand the dangers of e-cigarettes and conventional cigarettes to stop smoking. 2. RESEARCH METHOD 2.1. Research design This type of research is experimental research (True Experiment Research) by the method of the Post Test Only Control Group Design is to do the scoring on the level of alveolar cell damage after being treated. 2.2. Research samples The population and sample of this research are white male rats (Rattus norvegicus Wistar strain). The determination of the samples makes use of the formula Arifin WN & Zahiruddin WM (2017) with a sampling technique using simple random sampling, which found the total sample of 25 male rats (Rattus norvegicus Wistar strain). 2.3. Research group This study usesh the white rat as many as 25 rats were divided into five treatment groups. The first group (P0) is the negative control group (without exposure to e-cigarette and conventional cigarette smoke), the second group (P1) was given clover cigarettes smoke, the second (P2) was given filter cigarettes smoke, the third group (P3) was given e-cigarette smoke with nicotine omg, the fourth group (P4) was given ecigarette with 3mg nicotine levels. Clove and filter cigarettes in 5 cigarettes in exposure 2 times per day in a period of 30 days, while e-cigarettes smoke in 3mL liquid in exposure 2 times per day in a period of 30 days.
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2.4. Research procedure Stages smoke exposure carried out by first preparing a smoke exposure equipment. Smoking box (size 40x40x20 cm3) with 3 pumps, the first pump for clove and filter, a second pump for the e-cigarette and the third for free air pump where they all pump is set by Arduino. The pump will be piped to a conventional cigarette box smoking (clove and filters) and e-cigarette by using a hose. At the time of exposure to smoke, the smoking box is sealed with the supplied ventilation. Fifth mice simultaneously incorporated into the smoking box, then the box is closed again further conventional cigarettes and e-cigarette installed and the pump is turned so that the cigarette smoke into the box. After 30 days the rats will be anesthetized prior to surgery with chloroform 0,67ml. Lungs removed and cleaned of blood is then performed using 10% formalin fixation. After it was confirmed dead rats will be collected into one and buried. Lung organ that has been fixed histological preparations will be made by the method of paraffin and HE staining. Observations preparation using a microscope with 400x magnification and visual field observed at 5. The microanatomy structure of the lungs was analyzed by a score of the level of damage as listed in Table 1 and Table 2 [14]. Table 1. Score level alveolar cell damage Scoring
Histological
0
Pulmonary edema
no changes in histological structure
Destruction of the alveolar septum
no changes in histological structure
Infiltration of Inflammatory Cells
no changes in histological structure
1 pulmonary edema at less than a third of the field of vision Destruction of the alveolar septum at less than a third of the field of vision Infiltration of Inflammatory Cells at less than a third of the field of vision
2 pulmonary edema at onethird to two-thirds of the field of vision Destruction of the alveolar septum at onethird to two-thirds of the field of vision Infiltration of Inflammatory Cells at one-third to two-thirds of the field of vision
3 pulmonary edema in more than two-thirds of the field of vision Destruction of the alveolar septum more than two-thirds of the field of vision Infiltration of Inflammatory Cells more than two-thirds of the field of vision
A score of three parameters in each field of view will be averaged, the average yield to determine the extent of the damage. Then the rate of lung damage that has been obtained is classified based on the assessment criteria alveolar cell damage level consisting of: Table 2. Criteria for evaluation of alveolar damage Criteria Normal Minor damage Moderate damage Severe damage
Information There are no histological changes Pulmonary alveolar damage> 0% - < 30% of the maximum damage Pulmonary alveolar damage> 30% - < 60% of the maximum damage Pulmonary alveolar damage> 60% of maximal damage
value Variation 0 1 2 3
2.5. Data analysis research In this study, the data obtained from A score of three parameters in each field of view will be averaged, the average yield to determine the extent of the damage. Then the rate of lung damage that has been obtained is classified based on the assessment criteria alveolar cell damage level. Data will be processed using SPSS 24 and tested by Kruskal Wallis test and Mann-Whitney post hoc. 3.
RESULTS AND DISCUSSIONS Results of research on the differences in exposure to e-cigarette and ordinary cigarette against damage of the lung alveoli white male rats Rattus norvegicus obtained data on individual research groups in Table 3. In this study the state of the whole rat alive and there are no exclusion criteria so that all the samples are met, namely amounting to 25 animals. Data obtained in the form ordinal data that are divided into four categories ie normal alveolar damage, minor damage, moderate damage and severe damage where the data is obtained from the average of the value of the parameter scoring damage in the form of pulmonary edema, alveolar septal destruction, and inflammatory cell infiltration.
The effect of e-cigarette and a conventional cigarette to the alveolus on Wistar … (Budian Nurpangestu)
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ISSN: 2252-8814 Table 3. Alveolar damage observed data of each group Group K P1 P2 P3 P4
Normal 1 0 0 -
Minor damage 4 0 0 1 -
Moderate damage 4 3 4 4
Severe damage 1 2 1
Mean 3.40 15.90 17.80 12.00 15.90
Information: P0: Negative control group, only given food and drink standards for 30 days, P1: Rats were given food and drink standards and being exposed to clove cigarette smoke with 5 clove cigarettes in exposure 2 times a day for 30 days. P2: Rats were given food and drink standards and are exposed to filter cigarette smoke with 5 filter cigarettes in exposure 2 times a day for 30 days. P3: Rats were given food and drink standards and being exposed to ¬e-cigarette smoke with 0mg nicotine in 3mL liquid in exposure 2 times a day for 30 days. P4: Rats were given food and drink standards and being exposed to ¬e-cigarette smoke with 3mg nicotine in 3mL liquid in exposure 2 times a day for 30 days. Data from Table 3 is known that the average value of the group P0 alveolar damage (3.40) showed minor damage. The average value of alveolar damage at the P1 group (15.90) showed moderate damage, the average value of alveolar damage in group P2 (17.80) showed moderate damage, the average value of alveolar damage at P3 group (12,00) showed moderate damage. While the average value of alveolar damage in group P4 (15.90) showed moderate damage. In the calculation using the Kruskal Wallis p-value = 0.003 it showed p <0.005, it was concluded there is a significant difference in all groups. Statistical calculations of the Mann Whitney test were conducted to determine major differences between the treatment groups by looking at the larger value of each group. The results of the Mann Whitney test in Table 4 indicates a significant difference from P0-P1, P0-P2, P0-P3, P0-P4. While the results of the comparison between the treatment groups P1, P2, P3, P4 showed no significant difference. Histology and overview picture of alveolus lung damage criteria with 400x magnification shown in Figure 1 and Figure 2. Table 4. Statistical analysis comparing alveolar damage in each group (Post Hoc Mann Whitney test) Treatment group P0 P1 P2 P3 P4
(a)
P0 0.005 0.006 0.015 0.005
P1 0,005 0.513 0.180 1.000
P2 0.006 0.513 0.093 0.513
P3 0.015 0.180 0.093 0.180
(b)
P4 0.005 1.000 0.513 0.180 -
(c)
Figure 1. Histology picture alveolus lung damage criteria with 400x magnification (a) Pulmonary edema, (b) Destruction of the alveolar septum, (c) Inflammatory cell infiltration
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(b)
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(c)
Figure 2. Overview histology lung damage alveolus with 400x magnification (a) Minor damage, (b) Moderate damage, (c) Severe damage A significant difference between the control group and the treatment group because of the treatment groups give a conventional cigarette smoke exposure and the e-cigarettes smoke. The smoke from a cigarette can make the cilia paralyzed in a few hours, with repeated exposure eventually cause damage to the cilia. The inability of the cilia in sweeping out mucus containing foreign particles that constantly comes causing carcinogens inhaled to remain in the respiratory tract for a long time. In addition, cigarette smoke paralyzes the alveolar macrophages. Particles in cigarette smoke not only cripple the macrophages but also certain substances from tobacco smoke have a toxic effect directly on macrophages, which reduces the ability of macrophages to engulf foreign bodies. In addition, the toxic substances in tobacco smoke also cause irritation of the mucosal lining of the airways, causing excessive mucus production, and will indirectly partially clog the airways [15]. Smoking is one of the pollutants in the form of a gas containing a variety of chemicals including nicotine, carbon monoxide, tar and eugenol (clove cigarettes). Cigarette smoke is a source of free radicals can affect the metabolism of macrophages [16]. Alveolar macrophages stimulated by exposure to cigarette smoke will occur inactivation of α1-AT as proteinase inhibitor and secrete proinflammatory cytokines (TNF-α, IL-1, IL-2, IL-8, LTB4) in the lungs via two pathways, namely by way of metalloenzyme elastase production as having a role in α1-AT hydrolyze and produce reactive oxygen species (ROS) that will inhibit α1-AT. Elastase can damage lung structures of proteins, one of which is the destruction of the alveolar septum [17]. A significant difference between the treatment groups P1, P2, P3, P4 due to exposure to smoke conventional cigarettes and e-cigarette. In the treatment group, conventional cigarettes are nicotine, carbon monoxide causes barriers proliferation of fibroblasts and elastin tissue damage that causes dilation of the alveoli, whereas tar substances can cause lung damage and are carcinogenic substances. The content of the ecigarette smoke other than nicotine, carbon monoxide contained propylene glycol compound where after heated to produce formaldehyde which is a carcinogenic substance that is found also in conventional cigarettes. The content of the smoke conventional cigarettes and e-cigarettes cause lung histology damage [18]. Pulmonary edema may occur due to increased airway resistance due to exposure to foreign particles in cigarette smoke that occurs continuously or intravascular pressure high due to increased permeability of capillary endothelium resulting in extravasation of fluid in tissue interstitial quickly, therefore, occur obstacle air exchange in the alveoli are progressive, Increased capillary permeability will cause the activation of neutrophils and occurs aggregation of neutrophils, causing neutrophils inherent in endothelial cells and the release of free radicals, toxins and inflammatory mediators that cause alveoli filled by exudates rich in protein and contains many inflammatory cells including neutrophils, the primary source of protease cell are neutrophils, PMN cells, pulmonary alveolar macrophages (PAM), α1-AT that can occur destruction of elastic tissue becomes uncontrollable [19]. The imbalance of protease cell and elastase release by the working mechanism of inactivation leukoprotease inhibitor in particular the degradation of cells and the extracellular matrix will result in the integrity of elastic fibers and collagen is reduced so that the connective tissue is destroyed and the destruction of the septum alveolar, an increase in inflammatory cells such as neutrophils and macrophages because these pollutants from cigarette smoke cause proteolysis [20, 21]. 4.
CONCLUSION Based on the research showed there were no differences in lung alveolar damage in rats both exposed to e-cigarette and conventional cigarettes.
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ACKNOWLEDGEMENTS The author's wishes thank almighty God and the ministry of research, technology and higher education Indonesia who fund and support this research. Thank all the lecture in the University of Muhammadiyah Malang, all the laboratory staff in campus and hospital in the University of Muhammadiyah Malang, my parent, my family and all of my friends who participated in this research. REFERENCES [1] Infodatin, The Indonesian Society Smoking Behavior, Data and Information Center for the Ministry of Health of the Republic of Indonesia, Jakarta, pp. 12-15, 2015. [2] Riskesdas, Problems Smoking in Indonesia, Agency for Health Research and Development of the Republic of Indonesia, Jakarta, pp. 5-6, 2018. [3] Mudjiwijono HE, Nunuk SM, SN Beautiful, “Tomatoes Juice bronchus Ephitelial Reduce Cell in Rat with Chronic Sub Exposed to Cigarette Smoke,” Brawijaya Medical Journal, vol. 26(1), pp.1-3, 2010. [4] Tirtosastro S, Murdiyati A, The Chemical Ingredients of Tobacco and Cigarettes, Research and Development Ministry of Agriculture, Malang, pp. 33-43, 2010. [5] US Department of health and human services, How Tobacco Smoke Causes, MD: Public health services, Rockville, pp. 8-12, 2016. [6] FDA, Reporting Harmful and Potentially Harmful Constituents in Tobacco Products and Tobacco Smoke Under Section 904 (a) (3) of the Federal Food, Drug, and Cosmetic Act, the US Department of Health and Human Services Food and Drug Administration's Center for Tobacco products (CTP), USA, p. 4, 2012. [7] K. Palipudi, J. Mbulo, J. Morton, et al., “Awareness and current use of electronic cigarettes in Indonesia, Malaysia, Qatar, and Greece: Findings from the 2011-2013 Global Adult Tobacco Surveys,” Nicotine and Tobacco Research, vol. 3(2), pp. 1-7, 2015. [8] T. Bam, W. Bollow, I. Berezhnova, M. Jackson, A. Jones, and E. Latif, “Position Statement on the electronic cigarette or electronic nicotine delivery systems,” Int. J. Tuberc. Lung Dis., vol. 18(1), pp. 5-7, 2014. [9] M. William, A. Trtchounian, and P. Talbot, “Conventional and electronic cigarette (e-cigarette) smoking have different characteristics,” Nicotine Tobacco Res., vol. 12, pp. 905-912, 2010. [10] Department of Health, State Government of Indonesia, Indonesia Health Profile 2016, Jakarta: Ministry of Health RI, 2017. [11] J. Flora, N. Meruva, C. Huang, C. Walkinson, et al., “Characterization of potential impurities and degradation products in electronic cigarette formulations and aerosols,” Regulatory Toxicology and Pharmacology, vol. 74, p. 4, 2016. [12] N. Triana, S. Ilyas, and S. Hutahaean, “Histological Overview of Pulmo Male Mice (Mus musculus L.) After being exposed Smoke Electric (in Bahasa), Saintia Biology, vol. 1(2), pp. 1-7, 2013. [13] R. Tanuwihardja and A. Susanto, “Electronic cigarettes (electronic cigarettes),” Indonesia Respiratory Journal, vol. 32(1), pp. 53-61, 2012. [14] T. Hansel and P. Barnes, An Atlas of Chronic Obstructive Pulmonary Disease. Parthenon Publishing Group, London, pp. 22-36, 2004. [15] Sherwood L., Fisiologi Manusia dari Sel ke Sistem, Ed. 8. EGC, Jakarta, pp. 483-488, 2013. [16] M. Dietrich, B. Gladys, P. Edward, H. Mark, G. March, E. Carroll, and P. Lester, “Smoking and exposure to environmental tobacco smoke decrease some plasma antioxidants and increase of tocopherol in vivo after adjustment for dietary Antioxidants intakes,” Am. J. Clin. Nutr., vol. 77(1), p. 3, 2009. [17] V. Kumar, A. Abbas, and J. Aster, Robbins Basic Pathology, Ed. 9. Elsevier, Canada, pp. 465-481, 2013. [18] C. Varvadas, N. Anagnostopoulos, M. Kouglas, V. Evangelopoulou, G. Connolly, and P. Behrakis, “Short-term pulmonary effects of using an electronic cigarette: impacton respiratory flow resistance, impedance, and exhaled nitric oxide,” Chest, vol. 141(6), pp. 1400-1406, 2012. [19] D. Sargowo, D. Adiputro, M. Widodo, and R. Romdoni, “Extract of mangoosteen increases high-density lipoprotein levels in rats fed a high lipid,” Universa Medicina Journal, vol. 32(1), pp. 37-43, 2013. [20] M. Mohan, T. Dutt, and R. Ranganath, “Tobbaco smoking related interstitial lung diseases,” The Indian Journal of Chest Diseases and Allied Sciences, vol. 54(1), pp. 243-9, 2012. [21] A. Petta, “Histopathological characteristics of pulmonary emphysema in experimental models,” Einstein, vol. 12(3), pp. 382-383, 2014.
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Evaluation of shale volume and effective porosity using larionov and archie equations from wire-line logs, Niger delta Nigeria Mfoniso U. Aka, Johnson C. Ibuot, Francisca N. Okeke Department of Physics and Astronomy, University of Nigeria, Nigeria
Article Info
ABSTRACT
Article history:
In Niger Delta region of Nigeria, reservoirs are mostly loose and unstratified sands to hold fluids. In this paper, three different wells in central Niger Delta were assessed for shale volume and actual porosity. The results of the analysis delineate the presence of sand, sand-shale and shale formations. Hydrocarbon prospecting was found to be strong in sand, moderate in sandshale and shallow in shale respectively. However, existent of shale lessens effective porosity and water saturation of the rock formations. The extent of the formation extends from 1300 to 2500 m. Shale volume and actual porosity values extend from 0.00 to 0.302 dec and 0.047 to 0.302 dec which decrease with increasing depth. Comparably, the water saturation and water resistivity extend from 0.432 to 0.779 dec and 0.106 to 2.918 Ohm respectively. These values of actual porosity are strong in sand, moderate in sand-shale and shallow in shale formations. The results from this assessment proof well log a vital and easier tool in assessing of reservoir properties.
Received Jun 22, 2019 Revised Aug 20, 2019 Accepted Oct 2, 2019 Keywords: Central Niger delta Formation factor Porosity Well log
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Johnson C. Ibuot, Department of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria. Email: johnson.ibuot@unn.edu.ng
1.
INTRODUCTION Reservoirs in the Niger Delta region of Nigeria indicate a variety of complexities in sandstones, shaly and shale depositions which are loose and unstratified. The presence of shale in the formation within this region has effects on both petrophysical properties and logging tool responses, which reduces effective porosity of the reservoir [1]. Shale are laminated or fissile clastic sedimentary rock with predominance of clay and silt as the detrital components [2, 3]. It is classified into effective and passive shales as clayey, silty or sandy shales on the basis of texture. Effective shale contains montmorillionite and bentonite while passive shale contains kaolinite and chlorite with zero cation exchange capacities which can be identified only by neutron tool [4].Shale can be assigned in the formation in three ways: In the form of laminae between layers of sand which does not affect the porosity or permeability of the sand streaks. It can exist as grains or nodules in the formation matrix with similar properties of laminae shale and nearby massive shales. It can also be dispersed throughout the sand, partially filling the intergranular interstices. [5]. Porosity is a fraction of the volume of void over total volume between 0 and 1 or as a percentage between 0 and 100%. It is categorized as actual and total porosity. Actual porosity is portions of total void space that dispatch fluid. Total porosity is the percentage volume occupied by the pore space regardless of the type of fluid contained in the pore space. Information about core analysis technique is important in the assessment of reservoir parameters [6]. Problems such as low productivity of oil, well bore instability, decrease in depth formations and presence of clay particles in void space fount unreliability in depositional settings [7]. The formation evaluation problems are as a result of inadequate knowledge of the shale volume and effective porosity evaluation [8]. However, core analyses of sample from formations have been routinely used by some researcher in this region. Journal homepage: http://iaescore.com/online/index.php/IJAAS
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This method did not give much areal coverage information and cannot be used in all the rock formations, and this leads to persistence cases of low productivity of oil, well bore instability and decrease in the depth formation due to its coverage limitation outside the coring locations. The current research therefore, based mostly on well logs using Larionov and Archie equations for better evaluation of prospective areas. On the other hand, aid other properties such as lithology, water and hydrogen saturations. Also, information through these well log approaches will aid better identification of reservoir, non-reservoir units and enhance quick decision making in geological setting of the area. 2.
GEOLOGY OF THE STUDY AREA The area under study is the central Niger Delta region of Nigeria. It is situated on the continental margins of the Gulf of Guinea in equatorial West Africa [9]. The region is about 7500km 2 as the largest delta extending between longitude 30 and 90 East and latitude 40 and 60 North [9-13]. The Niger Delta is classified as a tropical rainforest with ecosystems comprising of diverse species of flora and fauna both aquatic and terrestrial species. The region could be classified into four ecological zones; coastal inland zone, freshwater zone, lowland rainforest zone, mangrove swamp zone and this region is considered one of the ten most important wetlands and marine systems in the world [14, 15]. Three major stratigraphic units have recognized in the onshore and offshore province, namely: Benin, Agbada and Akata formations. Benin formation which the study is housed comprises sand, gravel and swamp deposit [3]. Agbada formation comprises alternating sandstones [16]. It constitutes the main hydrocarbon habitant in the Niger Delta. Akata formation Eocene to Recent is made up of a sequence of under compacted marine clays with minor study and silty beds [17]. 3.
MATERIAL AND METHOD A total of three wire line logs data were analyzed, in order to evaluate lithology, shale volume and effective porosity respectively [18]. Fundamentally, high formation of gamma ray designates shale while low formation depicts sand [19]. However, lithology interpretation is the first step in well log analysis and very important in reservoir characterization. 3.1. Shale Volume (Ѵ ) Shale volume is the most important parameters in formation evaluation. It is expressed as shown in Larionov equations (1) to (3). Tertiary rocks: 𝑉 ℎ = 0.083(2
.
− 1)
(1)
Older rocks: 𝑉 ℎ = 0.33(2 𝛪
− 1)
(2) (3)
=
3.2. Porosity () Porosity measures the total amount of void space accessible from the surface. It is expressed as shown in Archie equations (4) and (5). 𝛷=
. .
𝛷=
(4) (5)
3.3. Effective Porosity Effective porosity is the pore volume in rock or sediments that contributes to permeability in a reservoir. It is expressed as shown in Archie equations (6) to (8).
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Sand reservoir: (6) Shale reservoir: (7) Shale-bound water: (8) 4.
RESULT AND DISCUSSION Sand and shale were the prevalent lithologies in Niger Delta region with staunch credibility significance parameters such as permeability, water and hydrocarbon saturation. In order to achieve this goal, three wells namely, wells 1, 2 and 3 were delineated into three zones, namely: 1, 2 and 3 which were modeled appropriately. Sand, sand-shale and shale were identified from the wells as shown in Figure 1 to Figure 3. Table 1 to Table 3 show the evaluation of petrophysical properties within the three wells; 1, 2 and 3 while Table 4 and Table 5 show the ranges of evaluated parameters and characterization of the well formations. In each of the well, three reservoirs where identified. In well 1, the result showed moderate values of the shale volume of 0.347 dec and effective porosity of 0.138 dec, with other parameters such as: mean porosity of 0.211 dec, permeability of 4.324 darc, water saturation and a resistivity value of 0.623 dec and 1.715 Ohmm with moderate hydrocarbon potential from sand-shale formation. In well 2, the result showed high values effective porosity of 0.302 dec and 0.0dec of shale volume with other parameters such as: mean porosity of 0.302 dec, permeability value of 3.847 darc, water saturation and resistivity of 0.432 dec and 2.18 Ohmm respectively. This indicates a high presence of hydrocarbon accumulation from sand formation. In well 3, the result showed high values of shale volume of 0.740 dec and low effective porosity of 0.047 dec with other parameters such as: mean porosity of 0.182 dec, permeability value of 6.454 dec, water saturation and resistivity of 0.799 dec and 2.985 Ohmm respectively. This indicates a low hydrocarbon accumulation from shale formation. However, it is observed that the value of the effective porosity and shale volume ranged from 0.047 to 0.302 dec and 0.182 to 0.302 dec. On the other hand, a higher hydrocarbon potential is allotted to sandy deposition found in well 2 with zero shale volume, bright spot formations. Moderate potential is allotted to sandy-shaly area found in well 1 while moderately potential is allotted to shaly area, less porosity and a higher shale volume found in well 3 depict clay sediments particles. Comparing the evaluated value with core sample analysis delineated much difference in core sample analysis values. This is due to the analytical and conservation sampling approaches compared with the standard values than that of evaluated approaches However, the foregoing comparison of evaluated approaches with core sample values affirms accuracy and applicability approaches using Larionov and Archie Equations in the study. Table 1. Well 1 Curves BVW CAL GR_NM K LL9D NPHI PHI RHOB RWapp SONIC SW VSH
Units Dec Inch API Darc gm/cc Dec Dec gm./cc Ohmm us/ft Dec Dec
Upper Values Lower Values 1223.05 3040.698 0 3519.7 0 3519.7 1223.05 3040.698 0 3519.7 0 3519.7 1223.05 3040.698 0 3519.7 1223.05 3040.698 0 3519.7 1223.05 3040.698 1223.05 3040.698
Difference Values 1817.702 3520.551 3520.551 1817.702 3520.551 3520.551 1817.702 3520.551 1817.702 3520.551 1817.702 1817.702
Minimum Values 0.000 -999.250 -999.250 2.053 -999.250 -999.250 0.000 -999.250 0.000 -999.250 0.046 0.119
Maximum Values 0.411 24.299 134.423 25.646 357.436 52.006 0.600 2.589 47.022 170.338 1.000 0.876
Mean Values 0.105 -340.605 36.216 4.324 -101.946 -478.577 0.211 -346.624 1.715 -279.431 0.623 0.347
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Curves BVW CAL LL9D GR_NM K NPHI PHI RHOB RWapp SONIC SW VSH
Units Dec Inch gm/cc API Darc Dec Dec Ohmm Ohmm us/ft Dec Dec
Upper Values Lower Values 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933 1200.023 2499.933
Difference Values 1300.080 1300.080 1300.080 1300.080 1300.080 1300.080 1300.080 1300.080 1300.080 1300.080 1300.080 1300.080
Minimum Values 0.021 11.750 1.745 26.702 2.221 0.409 0.050 0.750 0.010 58.200 0.064 0.000
Maximum Values 0.348 17.813 2.568 120.198 20.688 0.409 0.549 224.111 24.366 152.800 1.000 0.307
Mean Values 0.124 12.333 2.152 50.013 3.847 0.409 0.302 28.872 2.918 114.916 0.432 0.000
Minimum Values 0.019 12.344 2.034 26.361 3.996 0.128 0.019 1.175 0.002 57.625 0.241 0.717
Maximum Values 0.204 18.031 2.464 106.837 52.884 0.481 0.282 12.407 0.842 142.750 1.000 0.773
Mean Values 0.140 13.350 2.197 59.466 6.454 0.295 0.182 2.985 0.106 113.352 0.799 0.740
Table 3. Well 3 Curves BVW CAL LL9D GR_NM K NPHI PHI RHOB RWapp SONIC SW VSH
Units Dec Inch gm/cc gAPI Darc Dec Dec Ohmm Ohmm us/ft Dec Dec
Upper Values 2176.633 2176.633 2176.633 2176.633 2176.633 2176.633 2176.633 2176.633 2176.633 2176.633 2176.633 2176.633
Lower Values 2499.627 2499.627 2499.627 2499.627 2499.627 2499.627 2499.627 2499.627 2499.627 2499.627 2499.627 2499.627
Difference Values 323.141 323.141 323.141 323.141 323.141 323.141 323.141 323.141 323.141 323.141 323.141 323.141
Where: BVW = Bulk volume of water, CAL = Caliper log, GR_NM = gamma ray neutron meter, NPHI = Neutron porosity, PHI = porosity, RHOB = Resistivity density, RWapp = Apparent water resistivity, SONIC = Sonic log, SW = Water saturation, HS = Hydrogen saturation, VSH = Volume of shale, K= Permeability Table 4. Ranges of the evaluated shale volume, effective porosity and other parameters Parameter K (darc.) K (darc.) Φe (dec) SW (dec) K (darc.) RWapp(Ohmm)
Well 1 0.347 0.211 0.138 0.623 4.324 1.175
Well 2 0.000 0.302 0.302 0.432 3.847 2.180
Well 3 0.740 0.182 0.047 0.799 6.454 2.985
Table 5. Characterization of the well formations Wells 1 2 3
Characteristics Fair Fairly effective porosity, moderate shale volume and hydrocarbon accumulation Good Highly porous and good accumulation Weak highly shale volume and less accumulation
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Formations Sand- Shale Sand Shale
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Figure 1. Well log interpretation of Well 1
Figure 2. Well log interpretation of Well 2 Evaluation of shale volume and effective porosity using larionov and archie equations … (Mfoniso U. Aka)
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Figure 3. Well log interpretation of Well 3 5.
CONCLUSION The research and analysis carried out from three well delineate in central Niger Delta using Larionov and Archie equations. The result depicts three reservoirs extents 1300 to 2500m, Shale volumes and effective porosity extents 0.00 to 0.740dec and 0.047 to 0.302 dec. Comparably, water resistivity extent 0.432 to 0.779 dec. The evaluated shale volume and effective porosity were compared to core analysis data which give good agreement of the result. Also, prove the method a useful approach for assessment from wire line log data. ACKNOWLEDGEMENT The authors are grateful to their families for their support and encouragement. The editors and reviewers are also acknowledged. REFERENCES [1] I. O. Akpabio, C. I. Johnson, E. Okechukwu and T. Odunayo, “Petrophysical Characterization of Eight Wells from Wire line logs, Niger, Delta, Nigeria,” Asian journal of Applied Science, vol. 2(2), pp. 105-109, 2014. [2] G. E. Archie, “Classification of Carbonate Reservoir Rocks and Petrophysical Consideration,” American Association of Petroleum Geologist, Bulletin, vol. 4(2), pp. 10-18, 1952. [3] M. Mehana and I. El-Monier, “Shale Characteristics impact on Nuclear Resonance (NMR) fluid typing methods and correlations,” Petroleum, vol. 2(2), pp. 138-147, 2016 [4] N. T. Inyang, I. O. Akpabio, and O. E. Agbasi, “Shale Volume and Permeability of the Miocene Unconsolidated Turbidities Sands of Bonga Oil field, Niger Delta, Nigeria,” International journal of Advanced Geosciences, vol. 5(1), pp. 37-45, 2017. [5] K. J. Weber and E. M. Daukoru, “Petroleum Geology of the Niger Delta (9th World)” Petroleum Congress Proceeding: Tokyo, Japan, vol. 6(2), pp. 209-221, 1985.
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[6] M. Siyamak, M. Mohammed, K. G. Mohammed, and H. Elaheh, “Determination of Shale Volume Distribution Patterns and Effective Porosity from Well Log Data Based on Cross Plot,” World Multidisciplinary Earth Science Symposium, vol. 44, 2016. [7] O. A. Olafuyi and O. O. Omole, “A Study of Formation Resistivity Factor- Porosity Relationship in the Central Offshore of Niger Delta,” Journal of Economics and Engineering, 2010. [8] N. J. Inyang, E. E. Okwueze, and O. E. Agbasi, “Detection of Gas Sands in the Niger Delta by Estimation of Poisson Damping Factor (PDF) Using Wire Line Log Data,” Geosciences, vol. 5(1), pp. 46-51, 2015. [9] L. F. Awosika, “Impact of Global Climate change and Sea level rise on Resources and Energy Development in Nigeria. In: umolu. J.C., (Ed) Global Climate Change,” Impact on energy development DAMTECH Nigeria limited, vol. 62(4), pp. 5-11, 1995. [10] C. B. Powell, S. A. White, D. O. Ibiebele, M. Bara, B. Dut Kwiez, M. Isoun, and F. U. Oteogbu, “Oil Spill Environmental Impact: Effect on Aquatic Biology, Paper Presented at NNPC/FMHE,” International Seminar on Petroleum Industry and the Nigerian Environment, Kaduna, Nigeria, vol. 2(6), pp. 168-178, 1985. [11] CLO: Civil Liberties Organization, “Blood Trail: Repression and Resistance in the Niger Delta,” Ikeja Country Analysis Brief, vol. 4(2), pp. 9-14, 2002. [12] B. Anifowose, “Assessing the Impact of Oil and Gas Transport on Nigeria’s Environment,” U21 Postgraduate Research Conference Proceedings 1, University of Birmingham UK, vol. 10(2), pp. 40-46, 2008. [13] C. Chinweze and G. Abiolu- Oloke, “Women Issues, Poverty and Social Challenges of Climate Changes in the Nigerian Niger Delta,” Context 7th International Conference on the Human Dimension of Global Environmental Changes UN Campus, Bonne Germany, vol. 11(10), pp. 14-20, 2009. [14] FME: Federal Ministry of Environment, “Niger Delta Natural Resourses Damage Assessment and Restoration Project, Phase 1 Scoping Report,” Abuja: Federal Ministry of Environment, vol. 5(2), pp. 10-15, 2006. [15] ANEEJ, “Oil of Poverty in the Niger Delta,” A publication of the African Network for Environment and Economic Justice, vol. 4(2), pp. 53-59, 2004. [16] B. D. Evamy, J. Harembource, and P. Kamerly, “Hydrocarbon Habit of Tertiary Niger Delta,” American Association of Petroleum Geologist. Bulletin, vol. 6(2), pp. 1-29, 1978. [17] M. U. Igbokwe, R. V. Gurundha, and E. E. Okwueze, “Groundwater flow modeling of Kwa Ibo River Watershed,” South Eastern Nigeria Hydroelectric Process, vol. 3(11), pp. 1523-1531, 2008. [18] G. Asquith and Gibson, “Basic Well Log Analysis for Geologists,” American Association of Petroleum Geologist. Methods for Exploration Series, vol. 2(3), pp. 12-18, 1982. [19] R. E. Chapman, The logging of Boreholes: Petroleum Geology. Concise Study, Elsevier, Ameterdium, pp. 107-157, 1983.
Evaluation of shale volume and effective porosity using larionov and archie equations … (Mfoniso U. Aka)
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Survey of part-of-speech tagger for mixed-code Indian and foreign language used in social media Bhushan Nikam Department of Computer Science, Dr. D. Y. Patil ACS College, India
Article Info
ABSTRACT
Article history:
A Part-Of-Speech Tagger (POS Tagger) is a tool that scans the text in specific language and allocates chunks of speech to individual word (and another token), such as verb, adjective, nown etc., as more fine-grained POS tags are used in computational applications like 'noun-plural'. Basically, the goal of a POS tagger is to allocate linguistic (mostly grammatical) information to sub-sentential units, called tokens as well as to words and symbols (e.g. punctuation). This paper presents a survey of POS Tagger used for code-Mixed Indian and Foreign languages. Various methods, procedures, and features required to device POS Tagger for code-mixed foreign languages especially for Indian are studied and observations related to it are reported.
Received Apr 29, 2019 Revised Aug 28, 2019 Accepted Oct 6, 2019 Keywords: Information Extraction Machine Translation POS tools
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Bhushan Nikam, Department of Computer Science, Dr. D. Y. Patil ACS College, Sant Tukaram Nagar, Pimpri Colony, Pune, Maharashtra 411018, India. Email: bhushannikam1973@gmail.com
1.
INTRODUCTION Community language of communication in social media is often combined in nature, where individuals counterfeit their regional dialectal with English and this technique is found to be extremely popular. Natural language processing (NLP) work towards to gather the data from these texts somewhere Part-of-Speech (POS) tagging performs a key title role in receiving the prosody of the inscribed text. One purpose of POS labeling is to disambiguate homonyms. Several kinds of information including dictionaries, lexicons, rules etc. use by taggers. Word may be a member of more than one category. Lexicons have type or types of a specific word. For example, a word address is both verb and noun. Taggers utilizes the probabilistic evidence to solve this indistinctness of actual word. As a preprocessor in text processing POS tagger can be used. Text retrieval and indexing requires POS information. Language processing needs POS tags to choose the pronunciation. For making tagged corpora POS tagger is also used. Dialectal processing methods to code switched text was first accomplished in the early 1980s [1], whereas in social media text code-switching begun to be considered in the late 1990s [2]. Still, conventional texts code change was rare as to encourage ample curiosity by the computational dialectal research people, and it was first lately that, it emerges a study topic in its own right, with a code-switching workshop at EMNLP 2014 [3]. Solorio with Liu [4], projected a simple but well-designed solution of labeling mixed-code English-Spanish transcript twice - on one occasion for each language, a tagger - and then joining the outcome of the language-explicit taggers to get the optimal word-level tags [5]. For English-Hindi Mixed-Code Social Media Content, a POS Labeling System has been presented in [5]. Efforts has been performed on English-Bengal and English-Hindi data. Nelakuditi [6], performed, two different kinds of experiments, First, POS taggers based on machine learning and second is uniting POS taggers of individual languages [7].
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POS tagger tool has been designed for various languages, but for code-mixed Indian and foreign Languages, very little work yet is performed with undesirable accuracy. This paper presents review of such work which is prepared into next four Sections. Section 2 and 5 specifies techniques used and approaches involved in the implementation of POS tagger for code-mixed Indian and foreign dialects. Section 3 summarizes efforts made to implement CM POS tagger for Indian Languages. Challenges to implement code-mixed POS tagger is presented in section 4. 2.
VARIOUS APPROACHES AND TECHNIQUES USED TO IMPLEMENT CODE-MIXED POS TAGGER FOR INDIAN AND FOREIGN DIALECTS India is homegrown to number of dialects. Language changes and variety in dialect prompt frequent mixing of code in India. Hence, Indians are polyglot by habituation with necessity, and frequently change mix tongues in social media circumstances, that possess additional problems for automatic Indian social media text processing. Requirement for any kind of NLP applications especially in this context Code-Mixed Part-of-speech (CM-POS) labelling is essential. Relating to it, I present a report on various POS tagger approaches and techniques used to implement code-mixed POS tagger for Indian and foreign Languages. Jamatia and Das [5] experimented by using classification algorithms based on four machine learning technique to the undertaking exercise: Conditional Random Fields (CRF), with Sequential Minimal Optimization (SMO), Naïve Bayes (NB), and Random Forests (RF). For the Conditional Random Fields they tried the MIRALIUM1 application, whereas the other three were the applications in WEKA2 and reported effectuation on the complete dataset (2,583 utterances), after 5-fold cross-validation of all the ML methods using both fine-grained (FG) and coarse-grained (CG) tag sets and noticed that all the ML methods have further problems with HI-EN alternation. In the Machine learning based POS taggers experiment Nelakuditi et. al [6] used three types of Machine Learning techniques for designing the POS tagger viz, Support Vector Machines (SVM), Bayes classification (Bay) and Conditional Random Fields (CRF), with different groupings and distinctions. In second experiment of joining POS taggers of individual languages, CMU's Twitter POS tagger for English with POS tagger developed at LTRC, that is a part of the shallow parser tool3 for Telugu were used and then finally reported accuracies. Kamal Sarkar [7], developed HMM-based POS tagging system which is founded on Trigram Hidden Markov Model that uses data from the vocabulary, and some other word level attributes to improve the comment possibilities of the known along with unknown tokens. He gives in to scores for Hindi-English, Bengali-English and Tamil-English Language duos. His scheme has been skilled and tried on the datasets provided for ICON 2015 shared task. In the constrained mode, his technique gains average overall accuracy (averaged over all three language pairs) of 75.60% which is very close to other participating two systems (76.79% for IIITH and 75.79% for AMRITA_CEN) which ordered larger than his system. In the unrestricted mode, his system gets typical overall accuracy of 70.65% which is also nearby to the system (72.85% for AMRITA_CEN) that obtained average overall accuracy highest. Vyas et. al [8] conducted three different experiments: In the first experiment, by assuming the language identities and normalized/transliterated forms of the words, POS tagging is performed. It gives an idea of the accuracy of POS tagging task, if normalization, transliteration and language identification could be done perfectly. Experiments have been conducted with two different POS taggers for English: the Stanford POS tagger and the Twitter POS tagger. In the next experiment, by assuming that only the language identity of the words are known for Hindi their own model is applied to generate the back transliterations. For English, Twitter POS tagger is applied directly to handle social media text. In the third experiment by assuming nothing is known, language identifier process is first applied, and based on the language detected, Hi transliteration module, and Hi POS tagger, or the English tagger is applied and also stated that though the matrix information is not used in any of their experiments, it could be potentially useful for POS tagging which could be explored in future. For constrained and unconstrained training and result submission, Pimpale and Patel [9], used Stanford POS tagger and machine learning algorithm viz., Decision Tree J48, Decision Tree Random Forest, Naive Bayes and Multilayer Perceptron resp. By concluding, the method used is reporting well for constrained submission, but deficiency of the superiority working information doesn't allow doing ample with it, if they, use the distributed vector illustration of words in feature engineering, that allow them to use non-labeled data for working out. As stated by Sequiera et. al [10], explored machine learning approaches for Hindi (Hi)-English (En) CM typescript from social media POS tagging starting with repetition of the trials specified in [8] along with [4], and reconfirming results on dataset. Extending the attributes set applied by Solorio and Liu [4] and doing numerous feature selection experiments, they proposed and conducted a POS-tagging and joint Survey of part-of-speech tagger for mixed-code Indian and foreign language used in … (Bhushan Nikam)
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language labeling task. Their observations show that, when there is a marginal upgrading due to use of some supplementary features, joint modeling pointedly damages the results. Kamal Sarkar [11], also proposed a POS tagging system for social media texts. It is developed based on Conditional Random Fields (CRF) trained using a rich feature set that includes contextual features, orthographic features, punctuation features and word length features. He concluded that his system performs well across all three languages Bengali-English-Hindi pairs. He hoped that the proper choice of features along with the suitable grouping of machine learning algorithms would improve the performance of his system. According to Sharma and Motlani [12], experimented code-mixed POS tagging of Indian social media text using machine learning techniques. Building a POS tagger using constrained system, give them an accuracy of 75.04%, after being estimated on the new test dataset. While by using other resources, namely an unconstrained system, POS tagger did better than the constrained system and gives 80.68% of accuracy. For training and testing of both type of systems they used ten-fold cross-validation method and computed the best model attribute values by undertaking a grid search over all the parameters of the attributes. Finally, for the other two pairs, namely BN-EN (Bengali-English) and TA-EN (Tamil-English), accuracy measured was 79.84% and 75.48% respectively using developed and submitted constrained systems. Pipeline approach, for language identification, Back-transliteration and POS tagging Sisodiya [13] respectively used, logistic based classifier and CRF, Google API, and CRF++ based Hindi POS tagger developed by IIT Kharagpur. Singh and Kanskar [14] employed, controlled word-level classification with and without contextual signs, and sequence labeling using Conditional Random Fields, for implementation of a simple unconfirmed dictionary-based method. A modest dialectal discovery-based investigative used in which first, the text can be separated into portions of tokens belonging to a language, and then each portion be categorized according to its language and further labeled by the POS tagger for that dialectal. Linguistic finding and transliteration text is labeled through an English monolingual tagger and then selecting one out of two labels for a conversation based on some heuristics that was detected by several language detection techniques. As stated by Ghosh et. al [15], they listed various steps involved in POS labeling task using CRF++ toolkit and Stanford POS Tagger, including chunking, lexicons for dominant languages. They also concluded that Bengali-English and Hindi-English results are more than that of Tamil-English because of difference in labels used in Tamil-English gold standard files. Barman [16], divided the experiment into four parts viz., implementing, baselines for POS tagging, pipeline systems, their stacking systems and joint model. By performing with the data, five-fold crossvalidation and reported normal cross-validation exactness with investigating the use of hand-crafted features and attributes that can be gained from monolingual POS taggers (stacking), performed researches with different groupings of these attribute sets. They described a trilingual code-mixed corpus with POS comment. Using state-of-the-art methods performing POS tagging and investigating the usage of factorial CRF (FCRF)based joint model found that the best stacking method (S2) that practices the joint features, achieves better than the combine version (FCRF) and the systems with pipeline. They observed that combined modeling outperforms the systems with pipeline in their experimentations. FCRF fall late the best POS labeling system S2. Possibly, to achieve better performance than S2 more training data would help FCRF. According to Gupta et. al [17], they proposed a system that practices a comprehensive set of features for POS labeling. The feature set was used to design a POS model. Conditional random field (CRF) is applied as the underlying classifier. CRF++, an employment of CRF is used to accomplish the experiment. As CRF++ uses a stated feature template, therefore to discover the optimal feature template a series of experiments were made on the training data set in a cross-validated way. However, they tune the feature pattern on English-Hindi data set only and used the optimal model for all these CM languages (EnglishHindi, English-Bengali, and English-Telugu) pairs. Bhargava et. al [18, 19], experimented similar kinds of approaches to implement POS tagger for English-Telugu, English-Hindi, English-Bengali language pairs with a slight variation to achieve accuracies. Table 1 shows the summarizing efforts made to implement CM POS tagger for indian languages.
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Table 1. Summarizing efforts made to implement CM POS tagger for indian languages Languages English-Hindi English-Hindi
Year 2014 2015
English-Telugu
2015
Telugu Hindi, Bengal mixed with English
2016
Hindi-English
2015
Approches/ Algorithm CRF++, Twitter POS Tagger CRF, SMO, NB, RF SVM, Bayes classification, CRF, CMU's Twitter POS tagger, POS tagger developed at LTRC Stanford POS tagger, Decision Tree J48, Decision Tree, RF, NB, Multilayer Perceptron ML Algo. with several features set experiments, joint modeling
F1/ Accuracy 74.87% 64.91% 52.37% Ta+en 71.04, 48.03 Bn+En 75.46, Not Submitted Hi+En 71.11, 6.84 77.33%
Hindi-English, Bengali-English and Tamil-English
2015
HMM-based POS tagging method
In Constrained mode 75.60%. In an Unconstrained mode 70.65%
Bengali-English, Hindi-English and Telegu-English Bengali-English, Hindi-English and Telegu-English Bengali-English, Hindi-English and Telegu-English Bengali-English, Hindi-English and Telegu-English
2016
CRF trained using a rich feature set
79.99%
Hindi-English Bengali-English, Tamil-English
2015
English-Hindi
2015
Unconstrained Hi-En system 80.68% & constrained system for other 77.60% classifier using the CRF model 84.48%.
Hindi-English
2016
Bengali-English, Hindi-English, Tamil-English
2016
English-Bengali-Hindi
2016
ML approach, POS tagger using unconstrained system and constrained system logistic based classifier and CRF, Google API, CRF++ the dictionary-based approach, CRF, monolingual tagger, language detection techniques Stanford POS Tagger and CRF++ toolkit baselines for POS tagging, pipeline systems, stacking systems, factorial CRF based joint model
English-Hindi, English-Bengali and EnglishTelugu
2016
English-Telugu, English-Hindi, English-Bengali
English-Telugu, English-Hindi, English-Bengali
Rule-based tagging, CRF, CRF++
2016
RF and Extremely Randomized Tree
2016
Ran-dom forest, Logistic Regression, and Nave Bayes Ran-dom forest, Logistic Regression, and Nave Bayes RF, Logistic Regression, NB
75.22% accuracy in Bengali-English 84.58% on monolingual and 81.78% in code-mixed sentences -78.744 % in fined grained system 77.944 % for coarse-grained model F-Measure of Coarse-Grained Data Set C U Telugu-English 80.06 77.7 Hindi-English 71.03 71.655 Bengali-English 71.03 71.83 coarse-grained tag sets with an accuracy of 80.6% Coarse-grained 80.6%
3.
VARIOUS APPROACHES AND TECHNIQUES USED TO IMPLEMENT CODE-MIXED POS TAGGER FOR FOREIGN LANGUAGES Efforts are not much more still be seen to implement code-mixed POS tagger for foreign languages. Solorio and Liu [4] just predicted potential code alternation points, in the growth of extra accurate systems for processing code-mixed English-Spanish language. Such mixing of languages is rarely found all over the world, other than in India. 4.
CHALLENGES TO IMPLEMENT CODE-MIXED POS TAGGER Building Code-Mixed POS (CM-Part of Speech) taggers for Indian dialects is a particularly interesting problem in computational linguistics due to a lack of accurately glossed training corpora. More cultured language processing techniques are required for POS tagging that is proficient of drawing interpretations from more delicate dialectal information. From a dialectal outlook, meaning arises from the distinctness between dialectal units, including words, phrases, and so on. These distinctness are of two types: paradigmatic (concerning substitution) and syntagmatic (concerning positioning). To implement Code-Mixed POS tagger all these differences are also needed to be considered.
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5.
CONCLUSION The survey shows that in general, various Machine Learning techniques along with POS tagger are used by researchers to implement CM POS taggers for Indian and foreign languages. Much more work is started to perform for code-mixed Indian languages. But an actual tool for code-mixed POS tagging is not yet available on the internet. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]
Aravind K. Joshi, "Processing of sentences with intra-sentential code-switching," Proceedings of the 9th International Conference on Computational Linguistics, Prague, Czechoslovakia, pp. 145–150, 1982. John Paolillo, "Language choice on soc. Culture punjab," Electronic Journal of Communication, vol. 6(3), 1996. Thamar Solorio, et al., "Overview for the first shared task on language identification in code-switched data," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing 1st Workshop on Computational Approaches to Code Switching, Doha, Qatar, pp. 62–72, 2014. Thamar Solorio and Yang Liu, "Part-of-speech tagging for English-Spanish code-switched text," Proceedings of the Conference on Empirical Methods in Natural Language Processing, Honolulu, Hawaii, pp. 1051–1060, 2008. Anupam Jamatia, Björn Gambäckand, and Amitava Das, "Part-of-speech tagging code-mixed English-Hindi twitter and facebook chat messages," Recent Advances in Natural Language Processing (RANLP), pp. 239-248, 2015. Kovida Nelakuditi, Jittadivya Sai, and Radhika Mamidi, "Part-of-Speech Tagging for Code mixed English-Telugu Social media data," 17th International Conference on Intelligent Text Processing and Computational Linguistics Mexico, 2016. Kamal Sarkar, "Part-of-Speech Tagging for Code-mixed Indian Social Media Text," International Conference on Natural Language Processing, 2015. Y. Vyas, S. Gella, J. Sharma, K. Bali, and M. Choudhury, "Pos tagging of English-Hindi code-mixed social media content," In Proceedings of the First Workshop on Codeswitching, EMNLP, 2014. Prakash B. Pimpale and Raj Nath Patel, "Experiments with POS Tagging Code-mixed Indian Social Media Text," NLP Tools Contest on POS Tagging for Code-mixed Indian Social Media Text (POSCMISMT), 2015. R. Sequiera, M. Choudhury, and K. Bali, "POS Tagging of Hindi-English Code Mixed Text from Social Media: Some Machine Learning Experiments," ICON, pp. 237-246, Dec 2015. Kamal Sarkar, "A CRF Based POS Tagger for Code-mixed Indian Social Media Text," International Conference on Natural Language Processing, 2016. Arnav Sharma and Raveesh Motlani, "POS Tagging for Code-Mixed Indian Social Media Text: Systems from IIITH for ICON NLP Tools Contest," International Conference on Natural Language Processing, Dec 2015. Ayushman Sisodiya, Donthu Vamsi Krishna, and Sandeep Kumar Begad. "POS Tagging of Code Mixed Text, Project Report," IIT Kharagpur, 2015. Ajita Singh and Amit Kanskar. "POS Tagging of Hindi-English Code-Mixed Text from Social Media," International Journal of Science and Research (IJSR), vol. 5(10), Oct 2016. S. Ghosh, S. Ghosh, and D. Das, "Part-of-speech Tagging of Code-Mixed Social Media Text," Proceedings of the Second Workshop on Computational Approaches to Code Switching, Austin, TX, pp. 90-97, 2016. Utsab Barman, Joachim Wagner, and Jennifer Foster, "Part-of-speech Tagging of Code-mixed Social Media Content: Pipeline, Stacking and Joint Modeling," Proceedings of the Second Workshop on Computational Approaches to Code Switching, Austin, TX, pp. 30-39, 2016. Deepak Gupta, Shubham Tripathi, Asif Ekbal, and Pushpak Bhattacharyya, "SMPOST: Parts of Speech Tagger for Code-Mixed Indic SocialMedia Text," International Conference on Natural Language Processing, 2016. Rupal Bhargava, Bapiraju Vamsi Tadikonda, and Yashvardhan Sharma, "BITS_Pilani_Team2@POS Tagging for Code Mixed Indian Social Media," International Conference on Natural Language Processing, Dec 2016. R. Bhargava, R. Bhartia, I. Mishra, and Y. Sharma, "BITS_Pilani_Team1@POS Tagging for Code Mixed Indian Social Media," International Conference on Natural Language Processing, Dec 2016.
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International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 4, December 2019, pp. 269~278 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i4.pp269-278
269
Real time simulation of sensorless control based on back-EMF of PMSM on RT-Lab/ARTEMIS real-time digital simulator A. Idir1,3, A. Ahriche2, K. Khettab3, Y. Bensafia4, M. Kidouche5 1,2,5 Applied Automation Laboratory, Boumerdes University, Algeria Department of Electrical Engineering, University Mohamed Boudiaf of M'sila, Algeria 4 Department of Electrical Engineering, Bouira University, Algeria
3
Article Info
ABSTRACT
Article history:
Real-time simulation (RT) is very useful for rapid prototyping of complex and expensive systems using the high performance of a multiprocessor system. It has many applications in the field of testing controllers and protection systems under real conditions. In this article, Real-time simulations results of sensorless control of permanent magnet synchronous motor (PMSM) are presented. This simulator consists of two major subsystems, software with a Matlab / Simulink and hardware including FPGA boards for data acquisition, control boards and sensors. The two subsystems were coordinated together to achieve the simulation RT. To estimate the rotor position, a sliding mode observer (SMO) based on back emfs of the motor was implemented. The stability of the proposed method was verified using the concept of Lyapunov. A real-time system based on FPGA, is used for implementing and testing the algorithm for rotor position estimation based on back-emf tracking.
Received Apr 24, 2019 Revised Oct 3, 2019 Accepted Nov 1, 2019 Keywords: PMSM Real time simulation RT-Lab Sensorless control Sliding mode observer
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Abdelhakim Idir, Applied Automation Laboratory, BoumerdesUniversity, 1 Av. de l'Independance, 35000 Boumerdes, Algeria. Email: a.idir@univ-boumerdes.dz
1.
INTRODUCTION The Permanent-Magnet Synchronous Motor (PMSM) controlled by a converter of power electronics is a nonlinear system usually present complex. The main research areas in electrical drives include high-level integrated motor drive, new topologies of converter-inverter, new adjustable speed drives (ASD), optimizationof performance, control algorithms, and fault tolerant controllers design. Therefore, to perform tests at system level which is one of the principle subsystems in the development of a complex product and to maintain this development and prototyping costs at reasonable level, we need real-time (RT) simulations [1-5]. In addition, trying to reach technology and cost at optimium point, it pushes us to use a device that can be able of doing many parallel execution at the same time. DSPs are fast but it is necessary to do sequential calculation. If someone wants to build simulation in real time it is possible with DSP but it needs a DSP with very fast clock. Since the 1970s, Programmable logic arrays (PLAs) have been available but their applications were limited. Field-Programmable Gate Array offers more possibilities by the (FPGA) concept [6-8], introduced by Xilinx’ cofounder Freeman in 1984 [9]. RT-Lab simulator consists of two major subsystems, software with a Matlab / Simulink and hardware including FPGA boards for data acquisition, control boards and sensors. The two subsystems were coordinated together to achieve the simulation RT. Recently, there has been a lot of interest in the developpement ofsensorless algorithms in which the motor was controlled using the rotor angular speed estimated values [10, 11]. Several methods have been developed in order to estimate speed or position of the rotor, and among them are Extended Kalman Filter Journal homepage: http://iaescore.com/online/index.php/IJAAS
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(EKF), Sliding Mode Observer (SMO) and Flux Linkage Observer (FLO) [12]. The latter has a fast response, good robustness against themachine parameter variations and external disturbances [13, 14]. In sensorless control [15-21], some variables of machines are often not directly measurable, but their accurate knowledge is more than necessary for high-performance electrical drives control. Sensorless vector control scheme is successfully implemented if the the accuracy of the estimation of the rotor position is good. This algorithm is implemented by fundamental excitation method and the position of the rotor is detected from the back electromotive force (back EMF) [22, 23]. In this paper, a fully digital real-time simulation of a high performance of sensorlesscontrol of Permanent Magnet Synchronous Motor based on back-EMF estimator was presented. The validation and implementation of the proposed algorithm was reached through Opal RT's RT-Lab real-time simulation platform; able to perform calculations at time steps up to 10μs. This real-time simulation tool is now extensively employed by a great number of high-tech industries as a real-time laboratory package for rapid prototyping of complex control systems and for hardware-in-the-loop (HIL) applications. By the use of HIL simulations in the design process, overall cost can be reduced, development cycles reduced, costly breakdowns avoided, and interaction between different subsystems tested. 2.
PMSM MATHEMATICAL MODEL The field-based control framework presented in this paper is presented on a low voltage permanent magnet synchronous motor. To simplify the motor equations, the following hypotheses have been formulated [1, 19]: Magnetic flux distribution in the air gap is sinusoidal, Inductivityand resistivity are constant and equivalent in all phases, Hysteresis losses and Eddy currents are neglected and Lead of star point is not connected. Model of synchronous motor in (𝑑 − 𝑞) rotating frame can be described by (1) ⎧ ⎪ ⎨ ⎪ ⎩
=
𝑖
=
𝑖
−
+
𝑢
=
𝑖
−
+
𝑢
𝑐𝑜𝑠 𝜃 − 𝑖 𝑠𝑖𝑛 𝜃
(1) − 𝛺−
Where 𝑢 , 𝑢 , 𝑖 , 𝑖 are the (𝛼, 𝛽) components of stator voltage and current vectors, and 𝛺 = 𝑃𝜃 , 𝜃 are the mechanical angular speed and rotor position, 𝑅 , 𝐿 are stator resistance and inductance, 𝜙 is the flux generated by PMs, 𝐽 is moment of inertia, 𝐶 is electromagnetic torque and 𝑝 is the number of motor pole pairs. 𝑒 and 𝑒 are the stator back EMF components on (𝛼, 𝛽) frame defined by (2) 𝑒 = −𝜙 𝜔 𝑠𝑖𝑛 𝜃 𝑒 = 𝜙 𝜔 𝑐𝑜𝑠 𝜃
(2)
3. SLIDING MODE OBSERVER 3.1. Observer based on back EMF For the estimatation of the unmeasured mechanical quantities, we will develop an electromotive force (EMF)-based sliding modeobserver defined in (2). Assuming that the speed varies slowly [24, 25]. .
𝜔 ≈0
(3)
The EMF dynamics can be written as follows = −𝜔 𝑒 = −𝜔 𝑒
(4)
The SMO can be designed from the electrical equations in the fixed reference (𝛼, 𝛽) (1) and the back EMF dynamics (4)
Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 269 – 278
Int. J. of Adv. in Appl. Sci. .
𝑖
_
=
𝑖
−
_
+
𝑢
+ 𝐾 𝑠𝑖𝑔𝑛(𝑖
−𝑖
_
)
=
𝑖
−
_
_
+
𝑢
+ 𝐾 𝑠𝑖𝑔𝑛(𝑖
−𝑖
_
)
.
𝑖
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271
(5)
The EMF is given as follows .
𝑒 . 𝑒
= −𝑒 = −𝑒
_ _
𝜔 𝜔
_ _
+ 𝑘 𝑠𝑖𝑔𝑛(𝑖 + 𝑘 𝑠𝑖𝑔𝑛(𝑖
_ _
−𝑖 −𝑖
) )
_ _
(6)
Where, 𝑖 _ , 𝑖 _ are estimated currents and 𝑘 , 𝑘 are observer gains. The estimated speed can be calculated from (2) 𝜔
=
𝑒
𝛺
= 𝜔
+𝑒
_
(7)
𝑠𝑔𝑛( 𝐸 )
_
and 𝐸 is the back EMF on the axis (q)
Finally, the rotor position can be estimated as follow 𝜃
_
= 𝑎𝑟𝑐𝑡𝑎𝑛
_
(8)
_
3.2. Stability analysis A fast and accurate current regulator is essential to reacha good dynamic and static performance of sensorless control of the PMSM. The structure of the proposed control uses two sliding surfaces to regulate the stator current according to the fixed reference (𝛼, 𝛽) 𝑖 :𝑆 = 𝑖
−𝑖
_
(9)
𝑖 :𝑆 = 𝑖
−𝑖
_
(10)
When the variable structure control system operates in sliding mode, the switching control law ensures the condition 𝑆 = 𝑆 = 0. AL yapunovfunction is used to analysis the stability of the sliding mode observer 𝑉= 𝑆 𝑆=
𝑆
+𝑆
=
𝑖
−𝑖
+ 𝑖
_
−𝑖
_
(11)
)
Requisite condition for sliding mode observer stability is obtained as follows .
.
.
(12)
𝑉 =𝑆 𝑆 +𝑆 𝑆 ≤0 By subtracting (10) from (5) and (6), the estimation error equation is concluded .
𝑖
_
=
𝑖
−𝑖
_
−
_
− 𝐾 𝑠𝑖𝑔𝑛(𝑖
−𝑖
_
)
=
𝑖
−𝑖
_
−
_
_
− 𝐾 𝑠𝑖𝑔𝑛(𝑖
−𝑖
_
)
𝑖
−𝐾 𝑖
.
𝑖
(13)
Then .
𝑉=
𝑖
𝑖
−𝑖
−𝑖
−
_
−
_
𝑒
_
𝑒
_
𝑖
−𝑖
−𝑖
_
_
−𝐾 𝑖
−𝑖
−𝑖
_
𝑠𝑖𝑔𝑛(𝑖
_
𝑠𝑖𝑔𝑛(𝑖
−𝑖
−𝑖 _
_
)
)− (14)
From (14) we have 𝑖
−𝑖
_
−
𝑖
−𝑖
_
≤0
(15)
Real time simulation of sensorless control based on back-EMF of PMSM on RT-Lab/ARTEMIS … (A. Idir)
272
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.
𝑉 < 0, if 𝑒
_
𝑖
−𝑖
_
−𝐾 𝑖
−𝑖
_
−
𝑒
_
⥂
𝑖
−𝑖
_
−𝐾 𝑖
−𝑖
_
<0
(16)
Therefore, to keep the observer sliding modes stable, the observer gain should satisfy the following inequality 𝐾 > 𝑚𝑎𝑥
_
,
_
(17)
According to (17), the observer gain must be greater than the induced back EMF. 4.
PLATFORMOF RT-LAB REAL TIME The RT-LAB Simulator Architecture is shown in Figure 1. RT-LAB simulator includes: One or more target PC’s; one of the PCs (Master) operates the communication between the hosts and the targets and between all other target PC’s. The targets use the Quick unix (QNX) operating system in real time. One or more host PC’s permitting multiple users to access the targets; one of the hosts PC’s has simulator control fully, while other hosts, in read-only mode, can display and receive simulator signals in real time. Various Types of Inputs/Outputs (I/O’s); Input & Output (Digital & Analog), Pulse Width Modulation (PWM) in & out, timers, encoders, .etc. I/O’s can be managed by dedicated processors distributed [26] over several nodes.
Figure 1. RT-Lab simulator architecture 5.
REAL TIME HYBRID SIMULATION PRINCIPLE A PC-Cluster is a parallel multiprocessor computer system capable of meeting the real-time simulation performance requirements [1, 27]. Figure 2 shows the design of the real-time digital simulation of PMSM sensorless control. The real-time simulation is performed by running on separate processors (targets) and in parallel the speed and decoupling control module, the static converter module and the PMSM module. These three modules are actually C code (digital modules) obtained by an automatic code generator for realtime execution.
Figure 2. Real-time simulation of PMSM Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 269 – 278
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6. IMPLEMENTATION USING RT-LAB SIMULATOR 6.1. Organization of software development Figure 3 shows the proposed sensorless control of PMSM as implemented in Real time RT-Lab environment. The model is distributed over three target processor motherboards. The first two target processors operate at 2.4 GHz. The third, connected to others through a fast real-time Fire Wire link. The first CPU of the dual CPU unit calculates in real time the sliding mode observer and the decoupling unit of the rotor flux. The second calculates in real time the permanent magnet synchronous motor, the PWM signal generator and the voltage source inverter. The third processor is dedicated to data acquisition. The host PC is the console used for the control signals, the input reference, and the signal visualization.
Figure 3. Real-time model configuration of the PMSM Figure 4 shows the steps of control algorithm for real-time execution. In RT-Lab real time simulation, the first step is to group the model into sub-systems; the second step is the addition of the OpComm communication blocks which allow the activation and the saving of communication between host PC and target PC as well as between the different calculation nodes of a distributed simulation. The last step is to execute the model under RT-Lab according to the following steps (see Figure 4): open the model already created under Matlab/Simulink, then divide the global system into subsystems (model separation) and convert the Simulink model in real time via Real-Time Workshop (RTW) (specify exactly on which node of target will be executed each subsystem) and finally run the model on one or more QNX (Quick Unix) target. The C code is generated automatically for each subsystem for real-time execution [3]. Figure 5 shows the experimental setup of RT-Lab platform. The distributed configuration (multiple targets) allows complex models to be distributed on a parallel PC cluster. The real-time cluster is connected to the host PC through a TCP/IP Protocol. RT-LAB/MATLAB/SIMULINK
CONCEPTION
SEPARATION
REAL TIME WORKSHOP
CONVERSION
ASSIGNATION
LOADING
VARIABLE PROCESSING AND VISUALIZATION
RUNING PROGRAM FPGA
Figure 4. Steps of control algorithm for real-time execution Real time simulation of sensorless control based on back-EMF of PMSM on RT-Lab/ARTEMIS … (A. Idir)
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ISSN: 2252-8814
Figure 5. Experimental setup of LAA/UMBB Lab 7.
REAL TIME SIMULATION RESULTS Using sim power systems (SPS) toolbox, the sensorless control induction motor drive system has been modeled and built offline in Simulink environment. The offline Simulink model uses a variable step solver. For the improvement simulation speed, RT-Lab real-time platform uses a discretized fixed step time solver with a step size of 100𝜇𝑠 which is much smaller than what could be achieved by the most advanced DSPs. The workstation is connected to the real-time simulation platform via the Ethernet (TCP/IP) protocol. The target runs the model and the results are viewed and saved on the workstation, which is the front end interface. Figures 6 to Figure 8 show real-time simulation results of the sensorless control of the PMSM. The results of the sliding mode observer based on the estimation of back EMF show a good performance with small error estimation.
Speed (rpm)
1000 real speed reference speed
500 0 0
0.2
0.4
0.6
0.8
1
1.2
Time (sec)
1.4
1.6
1.8
2
10
real position estimated position
Zoom
5
0 0
0.5
1
Time (sec)
1.5
2
rotor position (rad)
rotor position (rad)
(a) 10
real position estimated position
5
0 1
1.02
1.04
1.06
Time (sec)
1.08
1.1
(b) Figure 6. Real time simulation: (a) Real and reference speed, (b) Real and estimated position
Figure 7. Real time simulation of estimated back EMF Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 269 – 278
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Speed (pu)
1 0.5
real speed reference speed estimated speed
0 0
0.2
0.4
0.6
0.8
1 Time (sec)
1.2
1.4
1.6
1.8
2
1
re al position e stimate d position
0.5
Z O O M
rotorposition(pu)
(a) 1 0.5 0 1 0 0
0.5
1 Time (sec)
1.05 1.5
1.1 2
Speederror
(b) 0.2
Errore al
0 -0.2 0
0.5
1 Time (sec)
1.5
2
(c) Figure 8. Real time simulation: a) Real, reference and estimated speed, b) Real and estimated position, c) Speed error The curves of real and estimated speed, position and currents respectively show the good responses. The results obtained from real time simulation show the efficiency of this powerful tool, wish is now widely used for Rapid Control Prototyping and Hardware in the Loop applications. RT-Lab/ARTEMIS simulation and model separation have significantly improved the simulation speed compared to Power System Blockset (PSB). Figure 9 shows the real time simulation of real and estimated currents. As shown in Figure 9 the estimated stator current components converge to the real stator current components, it’s clear that the waveforms of currents are sinusoid. From the experimental results, we concluded that that the sensorless control scheme associated with sliding mode observer has a fast response time and good estimation accuracy over a wide speed range. Table 1 shows the simulation time with PSB and RT-Lab / ARTEMIS. The article [24] shows the simulation conditions and processors used for the simulation of the separate model on the multiprocessor platform.
Figure 9. Real time simulation of real and estimated currents Real time simulation of sensorless control based on back-EMF of PMSM on RT-Lab/ARTEMIS … (A. Idir)
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ISSN: 2252-8814 Table 1. Simulation performance Simulation Mode Simulink/PSB 𝑻𝒔 = 𝟐 𝝁𝒔 RT-Lab/ARTEMIS 𝑻𝒔 = 𝟐 𝝁𝒔
Sampling Time
Acceleration
Real time report
500𝜇𝑠
1
250
34𝜇𝑠
14
15
8.
CONCLUSION A Real-time simulation of the sensorless control using a sliding mode observer based on the back EMF estimation has been presented in this paper. The stability of the proposed scheme has been demonstrated using Lyapunov concept. The feasibility of the whole algorithm has been verified by real time simulation results using RT-Lab/ARTEMIS real time digital simulator. Hardware applications in loops need real-time simulations and their use allows rapid prototyping of high-performance electrical machine controllers. A multi-processor system, parallel processing and FPGA-based computing support make this platform a very interesting tool for research, innovation and testing. High speed PMSM Implementation, especially in technology of electrical vehicle is very expensive and risky. Real time simulator helps us to evaluate simulation results. As future work, once the controller is designed in MATLAB/SIMULINK, it will be physically implemented using the rapid control prototyping of the real time RT-Lab platform. FPGA based digital platform is good enough for real time control of electrical machines. NOMENCLATURE RT LAB Real Time Laboratory ARTEMIS Advanced Real-Time Electro Mechanical Simulator PMSM Permanent Magnet Synchronous Motor FPGA Field-Programmable Gate Array FTP File Transfer Protocol HIL Hardware-in-the-Loop QNX Quick unix RCP Rapid Control Prototyping RTW Real-Time Workshop SMO Sliding Mode Observer EMF Electromotive force REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9]
M. Ouhrouche, R. Beguenane, A.M. Tzynadlowski, J.S. Thongam, and M. Dubé-Dallaire, "A PC-Cluster-based Fully Digital Real-Time Simulation of a Field-Oriented Speed Controller for an Induction Motor," International Journal of Modeling & Simulation, vol. 26(3), 2006. A. Idir and M. Kidouche, "Real-Time Simulation of V/F Scalar Controlled Induction Motor using RT- Lab Platform for Educational purpose," Proceedings of the International Conference on Systems, Control and Informatics, pp.189–192, 2013. O. Mohammed and N. Abed, "Real-time simulation of electric machine drives with hardware-in-the-loop," COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 27(4), pp. 929-938, 2008. A. Idir and M. Kidouche, "RT-Lab and "dSPACE: Tow Softwares for Real Time Control of Induction Motor," Rev. Roum. Sci. Techn. –Électrotechn. et Énerg, vol. 59(2), pp. 205–214, 2014. A. Ahriche, A. Idir, A. Boussoufa, M. Kidouche, and S. Mekhilef, "Real-time simulation of decoupled power control scheme for wind turbine applications by using RT-LAB package," 5th International Conference on Electrical Engineering (ICEE), Boumerdes, 2017. C. Dufour, J. Bélanger, S. Abourida, and V. Lapointe, "FPGA-Based Real-Time Simulation of Finite Element Analysis Permanent Magnet Synchronous Machine Drives," 38th Annual IEEE Power Electronics Specialists Conference (PESC), Orlando, FL. 2007. C. Dufour, V. Lapointe, J. Bélanger, and S. Abourida, "Hardware-in-theloop closed-loop experiments with an FPGA-based permanent magnet synchronous motor drive system and a rapidly prototyped controller," IEEE International Symposium on Industrial Electronics, Cambridge, UK. 2008. A. Darba, F. De Belie, T. Vyncke, and J. Melkebeek, "FPGA-based real time simulation of sensorless control of PMSM drive at standstill," IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Sorrento, 2012. J.J. Rodriguez-Andina, M.J. Moure, and M.D. Valdes, "Features, Design Tools, and Application Domains of FPGAs," IEEE Trans. Ind. Electron., vol. 54(4), 2007.
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[10] K.T. Ajmal and M.T. Rajappan Pillai, "Back EMF based sensorless BLDC drive using filtered line voltage difference," International Conference on Magnetics, Machines & Drives (iCMMD), AICERA, 2014. [11] A.E. Fitzgerald, C. Kingsley, and S.D. Umans, Electric Machinery. McGraw-Hill, 2003. [12] A. Ahriche, M. Kidouche, A. Idir, and Y. Deia, "Combining sliding mode and second lyapunov function for flux estimation," Rev. Roum. Sci. Techn. – Électrotechn. et Énerg, vol. 59(2), pp. 205–214, 2014. [13] T. Gao, "A sliding-mode observer design for the unknown disturbance estimation of a PMSM," The 27th Chinese Control and Decision Conference (CCDC), pp. 5851-5855, 2015. [14] J.M. Liu and Z.Q. Zhu, "Improved sensorless control of permanent magnet synchronous machine based on thirdharmonic back EMF," Proc. IEEE International Electric Machines & Drives Conference, pp.1180–1187, 2013. [15] F. Amin, E. Bin Sulaiman, W.M. Utomo, H.A Soomro, M. Jenal, and R. Kumar, "Modelling and Simulation of Field Oriented Control based Permanent Magnet Synchronous Motor Drive System," Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol.6(2), pp. 387-395, 2017. [16] X.C. Huang and R.W. Lin, "Novel Design for Direct Torque Control System of PMSM," TELKOMNIKA Telecommunication, Computing, Electronics and Control, vol. 11(4), pp. 2102-2109, 2013. [17] T. Sutikno, "The Preliminary Research for Implementation of Improved DTC Scheme of High Performance PMSM Drives," TELKOMNIKA Telecommunication, Computing, Electronics and Control, vol. 6, pp. 155-166, 2008. [18] V. Karthikeyan, "Dual Input Z-Source Inverter Fed PMSM Based Renewable Energy," Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 9(2), pp. 257- 261, 2018. [19] A. Idir, M. Kidouche, and A. Ahriche, "Vector Control of Permanent Magnet Synchronous Motor using RT-Lab Real Time Platform," International Conference on Automatic control, Telecommunications and Signals (ICATS15), Annaba, 2015. [20] A. Ahriche, M Kidouche, A Boussoufa, A. Idir, and S Mekhilef, "An Improved Speed Sensor-less Observer for high Performance AC Drives," International Conference on Technological Advances in Electrical Engineering, ICTAEE’16, 2016. [21] R.A. Ganapathy and K. R. Santha, "Review of Sliding Mode Observers for Sensorless Control of Permanent Magnet Synchronous Motor Drives," International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 9(1), 2018. [22] A. Darba, A. De Belie, T. Salem, and J. Melkebeek, "FPGA-Based Implementation of the Back-EMF SymmetricThreshold-Tracking Sensorless Commutation Method for Brushless DC-Machines," IEEE International Symposium on Sensorless Control for Electrical Drives and Predictive Control of Electrical Drives and Power Electronics (SLED/PRECEDE), 2013. [23] M. Murugan, R. Jayabharath, and C. Gurunathan, "Rotor Position Sensorless Control of BLDC Motor based on Back Emf Detection Method," Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 14(2), pp. 222-227, 2015. [24] P. Sicard, N. Elouariachi, N. Léchevin, and A. Ba-Razzouk, "Towards Real-Time Simulation on a PC-Cluster of Mechanically Coupled Induction Motors in a Material Transfer Process," Proceedings of the 7th international conference on modeling and simulation of electric machines, converters and systems (ELECTRIMACS), 2002. [25] M. Ezzat, J.D. Leon, N. Gonzalez, and A. Glumineau," Sensorless Speed Control of Permanent Magnet Synchronous Motor by using Sliding Mode Observer," 11th International Workshop on Variable Structure Systems (VSS), 2010. [26] R. Askour and B.B. Idrissi, "DSP-Based Sensorless Speed Control of a Permanent Magnet Synchronous Motor using Sliding Mode Current Observer," International Journal of Power Electronics and Drive System (IJPEDS), vol.4(3), pp. 281-289, 2014. [27] P. Jansen and R. Lorenz, "Transducerless position and velocity estimation in induction and salient AC machines," IEEE Trans. Industry Applications, vol. 31(2), pp. 240–247, 1995.
APPENDIX PMSM MOTOR PARAMETERS Three-phase Induction motor parameters, used for real-time implementation, in SI units are: 𝑅 = 2.5, 𝐿 = 3.42mH, 𝑝 = 4, 𝐼 A, 𝐽 = 0.00025 Kg.m2, 𝜙 = 0.47 Wb, 𝑓 = 0.05 Nm/rad.s-1 BIOGRAPHIES OF AUTHORS Abdelhakim Idir was born in Bejaia, Algeria. He received his B.S and M.S degrees in Control from the Bejaia University and Setif University in 2003 and 2006 respectively. He received his doctorate and HDR degrees in Electrical Engineering from the University M’Hamed Bougara of Boumerdes, Algeria, in 2015 and 2018, respectively. He currently works as an associate Professor in the Department of Electrical Engineering, Boumerdes University, Algeria. He is the author and co-author of numerous research publications in international conferences and journals. In 2019, he joined the Department of Electrical Engineering at the Mohamed Boudiaf University of M’sila, where he is currently working as an associate professor. His current research interests include the modeling, simulation and control of fractional systems, fractional PID control, AC drives and renewable energy.
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ISSN: 2252-8814 Dr. Aimad Ahriche was born in Algeirs (Algeria) in 1978. In 2014, he received his PhD degree in electrical engineering from university of Boumerdes (Algeria). His main researches are in Machine drives, power electronics and renewable energies. He is the author and Co-author of several scientific papers in the area. He is also reviewer with multiple international journals.
Khatir Khettab (khatir.khettab@univ-msila.dz) graduated from Ferhat Abbes University of Sétif (UFAS), Algeria, in 2001. He his MSc degree from UFAS, Algeria in 2005, and obtained his PhD degree in Advanced Automatic from Skikda University, Algeria in 2016. He currently works as an Assistant Professor (Lecturer) at the Mohamed Boudiaf University of M’sila, Algeria. He has many publications, and supervises the PhD theses. His research interests include robotics and automation, especially the robust fractional systems control, chaos synchronization, discrete-time fractional systems and fractional adaptive intelligent control.
Yassine Bensafia (bensafiay@yahoo.fr) was born in B´ejaia, Algeria, in 1978. He received the Engineering and Magister degrees in Electrical Engineering from the B´ejaia University, in 2003 and in 2006, respectively. Recently, he obtained his Science Doctorate in Automatic Control from the Department of Electrical Engineering, University of the 20th August 1955 of Skikda, Algeria. Since 2015, he joined the University of Bouira as an assistant professor (Lecturer). His research interests include Fractional systems control, Adaptive control, Robust control.
Kidouche Madjid was born in Bordj-Menaiel, Algeria. He received his Electrical Engineering, Master of Sciences, and Ph. D. degrees all in control theory. He joined M'hamedBougara University of Boumerdes, Algeria in 1990 where he is a Professor in the department of automation and electrification of industrial process. He is a research group head on "Control of complex dynamical systems" at Applied Automatic Control Laboratory. He has been actively involved in several research projects in the fields of control and power system analysis. He is the author and co-author of numerous research publications in international conferences and journals. His research interests include modeling and control of dynamic non linear systems, stability of large-scale systems, fuzzy and sliding mode control.
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International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 4, December 2019, pp. 279~284 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i4.pp279-284
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Solving optimal reactive power problem by improved variable mesh optimization algorithm Kanagasabai Lenin Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India
Article Info
ABSTRACT
Article history:
In this work Improved Variable Mesh Optimization Algorithm (IVM) has been applied to solve the optimal reactive power problem. Projected Improved VMO algorithm has been modeled by hybridization of Variable mesh optimization algorithm with Clearing-Based Niche Formation Technique, Differential Evolution (DE) algorithm. Mesh formation and exploration has been enhanced by the hybridization. Amongst of niche development process, clearing is a renowned method in which general denominator is the formation of steady subpopulations (niches) at all local optima (peaks) in the exploration space. In Differential Evolution (DE) population is formed by common sampling within the stipulated smallest amount and maximum bounds. Subsequently DE travel into the iteration process where the progressions like, mutation, crossover, and selection, are followed. Proposed Improved Variable Mesh Optimization Algorithm (IVM) 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 Apr 29, 2019 Revised Aug 12, 2019 Accepted Sep 9, 2019 Keywords: Clearing-based niche formation technique Differential evolution Optimal reactive power Transmission loss Variable mesh optimization algorithm
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Kanagasabai Lenin, Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Chalasani Nagar, 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 [7-14] 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. In this work Improved Variable Mesh Optimization Algorithm (IVM) has been applied to solve the optimal reactive power problem. Projected Improved VMO algorithm has been modeled by hybridization of Variable mesh optimization algorithm with Clearing-Based Niche Formation Technique, Differential Evolution (DE) algorithm. Mesh formation and exploration has been enhanced by the hybridization. Amongst of niche development process, clearing is a renowned method in which general denominator is the formation of steady subpopulations (niches) at all local optima (peaks) in the exploration space. Each niche has a leading (master) individual, i.e. the one with the most excellent fitness. In Differential Evolution (DE) population is formed by common sampling within the stipulated smallest amount and maximum bounds. Subsequent to the launch of generating the population, DE travel into the iteration process where the progressions like, mutation, crossover, and selection, are followed. Proposed Improved Variable Mesh Optimization Algorithm (IVM) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.
Journal homepage: http://iaescore.com/online/index.php/IJAAS
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ISSN: 2252-8814
PROBLEM FORMULATION Objective of the problem is to reduce the true power loss F=P =∑
g
∈
V + V − 2V V cosθ
(1)
Voltage deviation given as follows F = P + ω × Voltage Deviation
(2)
Voltage deviation given by Voltage Deviation = ∑
|V − 1|
(3)
Constraint (Equality) (4)
P =P +P Constraints (Inequality) P
≤P
(5)
≤P ,i ∈ N
(6)
Q
≤Q ≤Q
V
≤V ≤V
,i ∈ N
(7)
T
≤T ≤T
,i ∈ N
(8)
Q
≤Q ≤Q
,i ∈ N
(9)
3.
VARIABLE MESH OPTIMIZATION Variable mesh optimization algorithm (VMO) engendered population is scattered as a mesh. Mesh is poised of Z nodes (𝑚 , 𝑚 , . . , 𝑚 ) that symbolize the solutions in the search space [15]. Every node is oblique as a vector of M floating point numbers 𝑚 = 𝑔 , 𝑔 , . . , 𝑔 , . . , 𝑔 which designate the solution. In exploration procedure two methodologies called development and narrowing are utilized. During the development, new-fangled nodes are created in the direction of local maximum, comprehensive end and the boundary nodes. Grounded on an elite approach, nodes are prearranged bequeath to their superiority in ascending order. Then clear out adaptive operator is then applied; every node is evaluated to its successor to eradicate those that do not outdo the threshold. Threshold value is computed by ⎧ ⎪ ⎪ ⎪ 𝜀 =
⎨ ⎪ ⎪ ⎪ ⎩
, ,
𝑖𝑓 𝑑 < 0.149 % 𝐷
𝑖𝑓 0.149 % 𝐷 ≤ 𝑑 < 0.29% 𝐷 , ,
𝑖𝑓 0.29 % 𝐷 ≤ 𝑑 < 0.59% 𝐷
(10)
𝑖𝑓 0.59 % 𝐷 ≤ 𝑑 < 0.79% 𝑑 ,
𝑖𝑓 𝑑 ≥ 0.79% 𝐷
Maximum number of fitness assessment fixed by D and d symbolize the present number of fitness evaluation. Range 𝑘 , 𝑙 indicates the domain borders of every component. Node creation procedure at every cycle has been done. Commence For primary mesh randomly construct Z nodes In primary mesh choose the global best Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 279 – 284
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Replicate For every node in primary mesh do Discover adjoining k nodes by the spatial position Choose the premium neighbour by fitness values When present node is not the local most excellent then create a new-fangled node towards the local most excellent End if End for For every node in primary mesh however the global most excellent do Create a fresh node towards the global most excellent End for Engender nodes from the mesh boundary nodes According to fitness values categorize the nodes An adaptive clearing operator splodge Choose Z best nodes to construct the primary mesh for the successive iterations If essential capriciously engender new-fangled nodes to form the preliminary mesh for the subsequent iteration When end criterion is met, process will be stopped End 4.
CLEARING-BASED NICHE FORMATION TECHNIQUE Amongst of niche development process, clearing is a renowned method in which general denominator is the formation of steady subpopulations (niches) at all local optima (peaks) in the exploration space. Each niche has a leading (master) individual, i.e. the one with the most excellent fitness [16]. To a certain niche an individual fit in when its distance to the leading (master) individual is less than a given threshold called as clearing radius. This process share the possessions of a niche among a set of winners (individuals to be profited by clearing), whereas it sets to zero then the fitness of all erstwhile individuals will be in the same niche. Those restrained by the winner are deceitfully separated from the population. Subsequently reiterate this method for a definite number of iterations, then all winners will come into view. Start Arrange the population Pp in decreasing order with respect to the fitness values For i = 0 to S - 1 If (Fitness (Pp[i]) ≠ 0); nbWinners = 1 Generate a new-fangled niche, being Pp[i] its master For j = i + 1 to S – 1; if (Fitness (Pp[j]) ≠ 0 and Distance (Pp[i], Pp[j]) <σ) If (nbWinners < κ) nbWinners = nbWinners + 1 Place the individual Pp[j] in the present niche Else Fitness (Pp[j]) = 0 End if End for End 5.
DIFFERENTIAL EVOLUTION In Differential Evolution (DE) population is formed by common sampling within the stipulated smallest amount and maximum bounds [17]. Subsequent to the launch of generating the population, DE travel into the iteration process where the progressions like, mutation, crossover, and selection are followed. “DE/best/1” 𝐷 =𝑌
+ 𝐻(𝑌 − 𝑌 )
(11)
“DE/current-to-best/1” 𝐷 = 𝑌 + 𝐻(𝑌
− 𝑌 ) + 𝐻(𝑌 − 𝑌 )
(12)
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“DE/best/2” 𝐷 =𝑌
+ 𝐻(𝑌 − 𝑌 ) + 𝐻(𝑌 − 𝑌 )
(13)
“DE/rand/1” 𝐷 = 𝑌 + 𝐻(𝑌 − 𝑌 )
(14)
“DE/current-to-rand/1” 𝐷 = 𝑌 + 𝐻(𝑌 − 𝑌 ) + 𝐻(𝑌 − 𝑌 )
(15)
DE/rand/2” 𝐷 = 𝑌 + 𝐻(𝑌 − 𝑌 ) + 𝐻(𝑌 − 𝑌 )
(16)
Improved strategy of the binomial crossover described as follows 𝑔 =
𝑑 , 𝑖𝑓𝑟𝑎𝑛𝑑(0,1) ≤ 𝐸 𝑜𝑟 𝑙 = l 𝑦 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(17)
𝑌 =
𝐺 𝑖𝑓𝑓𝑖𝑡𝑛𝑒𝑠𝑠(𝐺 ) ≤ 𝑓𝑖𝑡𝑛𝑒𝑠𝑠(𝑌 ) 𝑌 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(18)
Begin Population are initialized Calculate the primary population For i=0 to max-iteration do Select capricious trial vectors Create off spring’s population Combine parent and offspring population If an offspring is greater than its parent then exchange the parent by offspring in the succeeding generation End if End for End 6.
IMPROVED VARIABLE MESH OPTIMIZATION ALGORITHM In this work Improved Variable Mesh Optimization Algorithm (IVM) has been hybridized with Clearing-Based Niche Formation Technique, DE algorithm. Mesh formation and exploration has been enhanced through the hybridization. Commence For primary mesh randomly construct Z nodes In primary mesh choose the global best Replicate For every node in primary mesh do Discover adjoining k nodes by the spatial position Choose the premium neighbour by fitness values When present node is not the local most excellent then create a new-fangled node towards the local most excellent End if End for For every node in primary mesh however the global most excellent do Create a fresh node towards the global most excellent End for Engender nodes from the mesh boundary nodes Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 279 – 284
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Apply the clearing-based niche formation technique For each identified niche do Sort nodes according to their fitness values Apply the adaptive clearing operator End for According to fitness values categorize the nodes An adaptive clearing operator splodge Choose Z best nodes to construct the primary mesh for the successive iterations If essential capriciously engender new-fangled nodes to form the preliminary mesh for the subsequent iteration DE call using VMO population When end criterion is met, process will be stopped End 7.
SIMULATION RESULTS At first in standard IEEE 14 bus system the validity of the proposed Improved Variable Mesh Optimization Algorithm (IVM) has been tested & comparison results are presented in Table 1. Table 1. Improved Variable Mesh optimization algorithm (IVM) Control variables V1 V2 V3 V6 V8 Q9 T56 T47 T49 Ploss (MW)
ABCO [18] 1.06 1.03 0.98 1.05 1.00 0.139 0.979 0.950 1.014 5.92892
IABCO [18] 1.05 1.05 1.03 1.05 1.04 0.132 0.960 0.950 1.007 5.50031
IVM 1.02 1.03 1.01 1.00 0.90 0.100 0.900 0.900 1.000 4.0986
Then IEEE 300 bus system [19] is used as test system to validate the performance of the Improved Variable Mesh Optimization Algorithm (IVM). Table 2 shows the comparison of real power loss obtained after optimization. Table 2. Comparison of real power loss Parameter PLOSS (MW)
Method EGA [20] 646.2998
Method EEA [20] 650.6027
Method CSA [21] 635.8942
IVM 613.1240
8.
CONCLUSION In this work Improved Variable Mesh Optimization Algorithm (IVM) has been successfully solved the optimal reactive power problem. Mesh formation and exploration has been enhanced by the hybridization. Amongst of niche development process, clearing is a renowned method in which general denominator is the formation of steady subpopulations (niches) at all local optima (peaks) in the exploration space. In Differential Evolution (DE) population is formed by common sampling within the stipulated smallest amount and maximum bounds. Proposed Improved Variable Mesh Optimization Algorithm (IVM) 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] K. Y. Lee, "Fuel-cost minimisation for both real and reactive-power dispatches," Proceedings Generation, Transmission and Distribution Conference, vol. 131(3), pp. 85-93, 1984. [2] N. I. Deeb, "An efficient technique for reactive power dispatch using a revised linear programming approach," Electric Power System Research, vol. 15(2), pp. 121-134, 1998. [3] M. R. Bjelogrlic, M. S. Calovic, and and B. S. Babic, "Application of Newton’s optimal power flow in voltage/reactive power control," IEEE Trans Power System, vol. 5(4), pp. 1447-1454, 1990.
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[4] S. Granville, "Optimal reactive dispatch through interior point methods," IEEE Transactions on Power System, vol. 9(1), pp. 136-146, 1994. [5] N. Grudinin, "Reactive power optimization using successive quadratic programming method," IEEE Transactions on Power System, vol. 13(4), pp. 1219-1225, 1998. [6] 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(1), pp. 61-67, 2006. [7] Aparajita Mukherjee and Vivekananda Mukherjee, "Solution of optimal reactive power dispatch by chaotic krill herd algorithm," IET Gener. Transm. Distrib, vol. 9(15), pp. 2351-2362, 2015. [8] Z. Hu, X. Wang, and Taylor, "Stochastic optimal reactive power dispatch: Formulation and solution method," Electr. Power Energy Syst, vol. 32, pp. 615-621, 2010. [9] M. Morgan, Nor Abdullah, Mohd Sulaiman, M. 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(14), 2016. [10] 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. [11] Mahaletchumi Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman, Mahfuzah Mustafa, and 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, pp. 12-1, 2016. [12] Rebecca Ng Shin Mei, Mohd Herwan Sulaiman, and Zuriani Mustaffa, "Ant Lion Optimizer for Optimal Reactive Power Dispatch Solution," Journal of Electrical Systems Special Issue AMPE 2015, pp. 68-74, 2016. [13] A. Gagliano and F. Nocera, "Analysis of the performances of electric energy storage in residential applications," International Journal of Heat and Technology, vol. 35(1), pp. S41-S48, 2017. [14] M. Caldera, P. Ungaro, G. Cammarata, and G. Puglisi, "Survey-based analysis of the electrical energy demand in Italian households," Mathematical Modelling of Engineering Problems, vol. 5(3), pp. 217-224, 2018. [15] A. Puris, R. Bello, D. Molina, and F. Herrera, "Variable mesh optimization for continuous optimization problems," Soft Comput., vol. 16, pp. 511-525, 2018. [16] K. Price, R. M. Storn, and J. A. Lampinen, "Differential Evolution: A Practical Approach to Global Optimization," Springer, 2006. [17] A. Pétrowski, "A clearing procedure as a niching method for genetic algorithms," Proc. 3rd IEEE International Conference on Evolutionary Computation, Nagoya, Japan, pp. 798-803, 1996. [18] Chandragupta Sivalingam, Subramanian Ramachandran, and Purrnimaa Shiva Sakthi Rajamani, "Reactive power optimization in a power system network through metaheuristic algorithms," Turkish Journal of Electrical Engineering & Computer Science, vol. 25, pp. 4615-4623, 2017. [19] IEEE, "The IEEE-test systems," 1993. [Online]: Available: http://www.ee.washington.edu/trsearch/pstca/ [20] 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. [21] S. Surender Reddy, "Optimal reactive power scheduling using cuckoo search algorithm," International Journal of Electrical and Computer Engineering, vol. 7(5), pp. 2349-2356. 2017.
Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 279 – 284
International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 4, December 2019, pp. 285~289 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i4.pp285-289
285
Real power loss reduction by dolphin swarm algorithm Kanagasabai Lenin Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India
Article Info
ABSTRACT
Article history:
In this work Spinner Dolphin Swarm Algorithm (SDSA) has been applied to solve the optimal reactive power problem. Dolphins have numerous remarkable natural distinctiveness and living behavior such as echolocation, information interactions, collaboration, and partition of labor. Merging these natural distinctiveness and living behavior with swarm intelligence has been modeled to solve the reactive power problem. Proposed Spinner Dolphin Swarm Algorithm (SDSA) 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 Apr 29, 2019 Revised Aug 20, 2019 Accepted Nov 2, 2019 Keywords: Optimal reactive power Transmission loss Spinner dolphin
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Kanagasabai Lenin, Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Chalasani Nagar, 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. In this work Spinner Dolphin Swarm Algorithm (SDSA) has been applied to solve the optimal reactive power problem. The whole process of dolphin’s predation consists of three stages. In the primary phase, every dolphin separately takes benefit of sounds to explore for close by preys and to assess the nearby environment using echoes. In the second phase, dolphins swap their information. When dolphins received information then it moves towards the prey and it has been surrounded by other dolphins. In the final phase, the prey is encircled by the dolphins to consume the food; it indicates that predation is accomplished. Proposed Spinner Dolphin Swarm Algorithm (SDSA) 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 Voltage deviation given as follows
Journal homepage: http://iaescore.com/online/index.php/IJAAS
(1)
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𝐅 = 𝐏𝐋 + 𝛚𝐯 × 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧
(2)
Voltage deviation given by 𝐍𝐩𝐪 𝟏 |𝐕𝐢
𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧 = ∑𝐢
− 𝟏|
(3)
Constraint (Equality) (4)
𝐏𝐆 = 𝐏𝐃 + 𝐏𝐋 Constraints (Inequality) 𝐦𝐢𝐧 𝐦𝐚𝐱 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 ≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 ≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤
(5)
𝐦𝐚𝐱 𝐐𝐦𝐢𝐧 , 𝐢 ∈ 𝐍𝐠 𝐠𝐢 ≤ 𝐐𝐠𝐢 ≤ 𝐐𝐠𝐢
(6)
𝐕𝐢𝐦𝐢𝐧 ≤ 𝐕𝐢 ≤ 𝐕𝐢𝐦𝐚𝐱 , 𝐢 ∈ 𝐍
(7)
𝐓𝐢𝐦𝐢𝐧 ≤ 𝐓𝐢 ≤ 𝐓𝐢𝐦𝐚𝐱 , 𝐢 ∈ 𝐍𝐓
(8)
Q
(9)
≤Q ≤Q
,i ∈ N
3.
SPINNER DOLPHIN SWARM ALGORITHM Spinner Dolphin Swarm Algorithm (SDSA) is employed primarily by replicating the natural features and living behaviour by a dolphin. In this work 𝐷𝑂𝐿𝑃𝐻𝐼𝑁 = [𝑥 , 𝑥 , . . , 𝑥 ] 𝑖 = (1,2, . . , 𝑁), where N is the number of dolphins and 𝑥 (𝑗 = 1,2, . . , 𝐷) component to be optimized [17]. Individual optimal solution (indicated as L) and neighbourhood optimal solution (indicated as K) are two variables connected with the dolphin. For each 𝐷𝑂𝐼 (i=1, 2, …, N), there are two corresponding variables Li (i=1, 2, …, N) and Ki (i=1, 2, …, N), where Li symbolize the optimal solution that 𝐷𝑂𝐼 finds in a distinct time and Ki the optimal solution of what 𝐷𝑂𝐼 locate by itself. In the proposed algorithm, there are three types of distances are utilized as in sum. The primary is the distance between 𝐷𝑂𝐼 and 𝐷𝑂𝐼 named 𝐷𝐷 , which is designed as follows 𝐷𝐷 , = 𝐷𝑂𝐼 − 𝐷𝑂𝐼
𝑖, 𝑗 = 1,2, . . , 𝑁, 𝑖 ≠ 𝑗
(10)
𝐷𝐾 = ‖𝐷𝑂𝐼 − 𝐾 ‖ 𝑖 = 1,2, . . , 𝑁
(11)
𝐷𝐾𝐿 = ‖𝐿 − 𝐾 ‖ 𝑖 = 1,2, . . , 𝑁
(12)
In exploration phase, every dolphin explores its close proximity area by creation of sounds towards M arbitrary directions 𝑋
(13)
= 𝐷𝑂𝐼 + 𝑉 𝑡
Fitness value is computed as follows, 𝐸
(14)
= 𝐹𝑖𝑙𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 (𝑥𝑗𝑡)
When, 𝐸 = 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 = 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 , ,..,
, ,.., ; ;
, ,..,
𝐸 𝐹𝑖𝑙𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 (𝑥𝑗𝑡)
, ,..,
(15)
Individual solution is determined by 𝐿 =𝑋 Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 285 – 289
(16)
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𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 𝐿 < 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 𝐾
(17)
𝑇𝑆 , = 0
(18)
𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 𝐾 > 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 𝐾
(19)
287
Transmission time matrix TS will be modernized as follows 𝑇𝑆 , >
,
(20)
,
(21)
.
Modernized by 𝑇𝑆 , =
.
Search radius is represented by 𝑅 = 𝑇 × 𝑠𝑝𝑒𝑒𝑑
(22)
𝐷𝐾 ≤ 𝑅
(23)
Encircling radius can be computed by 𝑅 = 1−
(24)
𝐷𝐾 , 𝑒 > 2
𝑁𝑒𝑤 𝐷𝑂𝐼 = 𝐾 +
(25)
𝑅
Updated value known by 𝐷𝐾 > 𝑅
(26)
𝐷𝐾 ≥ 𝐷𝐾𝐿
(27)
The encircling radius R2 can be computed as follows 𝑅 = 1−
𝑅 = 1−
.
.
𝐷𝐾 , 𝑒 > 2
(28)
𝐷𝐾 , 𝑒 > 2
(29)
New-fangled positions of 𝑁𝑒𝑤 𝐷𝑂𝐼 after obtaining the encircling radius, 𝑁𝑒𝑤 𝐷𝑂𝐼 = 𝐾 +
‖
‖
(30)
𝑅
(31)
𝐷𝐾 < 𝐷𝐾𝐿 For new position the fitness value can be calculated by, 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒(𝑛𝑒𝑤 𝐷𝑂𝐼 ) < 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 𝐾
(32)
Step 1: initialize arbitrarily and consistently engender the preliminary of dolphin swarm Dol = {Dol 1, Dol2, …, DolN} in the D-dimensional space. Compute the fitness value for every dolphin, and acquire Fitness valueK = {Fitness value K,1, Fitness value K,2, …, Fitness value K,N}. Step 2: commencement of loop While the stop condition is not satisfied do Real power loss reduction by dolphin swarm algorithm (K. Lenin)
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Step 2.1: exploration phase 𝐸
= 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 (𝐷𝑂𝐼 + 𝑉 𝑡)
Fitness value L={min{E1jt}, min{E2jt}, …, min {ENjt}} 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒
=
,
𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒
, 𝑖𝑓 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 , < 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
,
Step 2.2: call phase ,
𝑇𝑆 , =
𝑖𝑓 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒
.
< 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒
,
,
𝑎𝑛𝑑 𝑇𝑆 , >
,
.
𝑇𝑆 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Step 2.3: reaction phase TSi,j reduce one unit time 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒
=
,
𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒
,
𝑖𝑓 𝑇𝑆 , = 0 𝑎𝑛𝑑𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 , < 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒 𝐹𝑖𝑡𝑛𝑒𝑠 𝑣𝑎𝑙𝑢𝑒 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
,
Step 2.4: predation phase Compute DKi and DKLi if DKi ≤ Rl 𝑅 = 1−
𝐷𝐾 , 𝑒 > 2
Else if DKi ≥ DKLi 𝑅 = 1−
𝐷𝐾 , 𝑒 > 2
.
Else, 𝑅 = 1−
𝐷𝐾 , 𝑒 > 2
.
End if 𝐷𝑂𝐼 gets a new-fangled position, compute its fitness value, and modernize Fitness valueK,i End While Output the most excellent one of Ki (i=1, 2, …, N) 4.
SIMULATION RESULTS At first in standard IEEE 14 bus system the validity of the proposed Spinner Dolphin Swarm Algorithm (SDSA) has been tested & comparison results are presented in Table 1. Table 1. Comparison results of the proposed spinner dolphin swarm algorithm Control variables V1 V2 V3 V6 V8 Q9 T56 T47 T49 Ploss (MW)
ABCO [18] 1.06 1.03 0.98 1.05 1.00 0.139 0.979 0.950 1.014 5.92892
IABCO [18] 1.05 1.05 1.03 1.05 1.04 0.132 0.960 0.950 1.007 5.50031
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SDSA 1.05 1.02 1.00 1.03 0.90 0.100 0.900 0.900 1.000 4.0192
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Then IEEE 300 bus system [19] is used as test system to validate the performance of the Spinner Dolphin Swarm Algorithm (SDSA). Table 2 shows the comparison of real power loss obtained after optimization. Table 2. Comparison of real power loss Parameter PLOSS (MW)
Method EGA [20] 646.2998
Method EEA [20] 650.6027
Method CSA [21] 635.8942
SDSA 613.1010
5.
CONCLUSION In this work Spinner Dolphin Swarm Algorithm (SDSA) has been successfully solved the optimal reactive power problem. The biological characteristics of spinner dolphin and its living behaviour have been imitated to model the algorithm; which are explore phase, call phase, reaction phase, and predation phase. Proposed Spinner Dolphin Swarm Algorithm (SDSA) 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] [18] [19] [20] [21]
K. Y. Lee, "Fuel-cost minimisation for both real and reactive-power dispatches," Proceedings Generation, Transmission and Distribution Conference, vol. 131(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(2), pp. 121-134, 1998. M. R. Bjelogrlic, M. S. Calovic, and B. S. Babic, "Application of Newton’s optimal power flow in voltage/reactive power control," IEEE Trans Power System, vol. 5(4), pp. 1447-1454, 1990. S. Granville, "Optimal reactive dispatch through interior point methods," IEEE Transactions on Power System, vol. 9(1), pp. 136-146, 1994. N. Grudinin, "Reactive power optimization using successive quadratic programming method," IEEE Transactions on Power System, vol. 13(4), pp. 1219-1225, 1998. W. 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(1), pp. 61-67, 2006. Aparajita Mukherjee and Vivekananda Mukherjee, "Solution of optimal reactive power dispatch by chaotic krill herd algorithm," IET Gener. Transm. Distrib., vol. 9(15), pp. 2351-2362, 2015. Z. Hu, X. Wang, and Taylor, "Stochastic optimal reactive power dispatch: Formulation and solution method," Electr. Power Energy Syst., vol. 32, pp. 615-621, 2010. M. A/P Morgan, N. Hasma Abdullah, M. Sulaiman, M. Mustafa, and R. 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(14), 2016. K. Pandiarajan and C. K. Babulal, "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, et al., "Benchmark Studies on Optimal Reactive Power Dispatch (ORPD) Based Multiobjective Evolutionary Programming (MOEP) Using Mutation Based on Adaptive Mutation Adapter (AMO) and Polynomial Mutation Operator (PMO)," Journal of Electrical Systems, pp. 12-1, 2016. Rebecca Ng Shin Mei, Mohd Herwan Sulaiman, and Zuriani Mustaffa, "Ant Lion Optimizer for Optimal Reactive Power Dispatch Solution," Journal of Electrical Systems, Special Issue AMPE2015, pp. 68-74, 2016. A. Gagliano and F. Nocera, "Analysis of the performances of electric energy storage in residential applications," International Journal of Heat and Technology, vol. 35(1), pp. S41-S48, 2017. M. Caldera, P. Ungaro, G. Cammarata, and G. Puglisi, "Survey-based analysis of the electrical energy demand in Italian households," Mathematical Modelling of Engineering Problems, vol. 5(3), pp. 217-224, 2018. A. Puris, R. Bello, D. Molina, and F. Herrera, Variable mesh optimization for continuous optimization problems. Soft, 2011. K. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization. Springer, 2006. A. Kaveh and N. Farhoudi, "A new optimization method: Dolphin echolocation," Advances in Engineering Software, vol. 59, pp. 53-70, 2013. Chandragupta Sivalingam, Subramanian Ramachandran, and Purrnimaa Rajamani, "Reactive power optimization in a power system Comput.," vol. 16, pp. 511-525. IEEE, "The IEEE-test systems," 1993. [Online] Available: http://www.ee.washington.edu/trsearch/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(5), pp. 2349-2356. 2017.
Real power loss reduction by dolphin swarm algorithm (K. Lenin)
International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 4, December 2019, pp. 290~292 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i4.pp290-292
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Optimization of transmission signal by artificial intelligent Hassan Farhan Rashag, Mohammed H. Ali Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Iraq
Article Info
ABSTRACT
Article history:
In this method, radial basis function network RBFNN is an artificial intelligent which is used to identify and classify the communication system performance. RBFNN is one type of neural network which has activation functions. It consists of three-layer input layer, hidden layer and output linear combination. One of the main problems of communication system is that it causes slow response for sending signal via the transmission devices. Therefore, the artificial intelligent by RBFNN is used to optimize the transmission signal. The input signal is trained and testing by neurons with weight and this lead to provide linear output. The simulation results have the optimization specifics over the traditional communication transmission devices.
Received Apr 17, 2019 Revised Jul 22, 2019 Accepted Aug 3, 2019 Keywords: Artificial intelligent Matlab RBFNN Transmission devices
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Hassan Farhan Rashag, Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Kufa, Iraq. Email: Hassan_rashag@yahoo.com
1.
INTRODUCTION The concept of communication system is that sending the message at one point either exactly or approximately to another point [1]. In addition, the reliably of transmitting the message from the transmission source to a destination system based on channel by transmitter and receiver devices is discussed [2]. In order to derive the theoretically optimal solution in practice, source and receiver were consequently separated into numerous treating systems. This application is called sub-optimal [3, 4]. The sub-optimal has the benefit that any element can be separately evaluated and improved which gets actual effective and constant systems which is existing nowadays. Many reports focused on the optimization for transmit the signal with more effective [5-7]. Though recently, structures are improved over the previous years and it appears hard to achieve good system but any kind of station no need for calculated exhibiting with examination. [8, 9]. Other researchers developed method to enhance the signals features by using multiple cascades for transmission devices but this method has drawbacks like slow response and distorted carrier waves [10-12]. 2.
SIMULATION RESULTS In this method, the RBFNN is chosen to optimize the transmission signals because it has many features over the neural network. These features are classification and identification the input signal by input and hidden layer to provide linear output signal. The structure of RBFNN is shown in Figure 1. In addition, the receiver constellation with equalizer learning curve based on bit error rate BER is shown in Figure 2. From this figure, the system is almost enhanced at BER = 0.49.
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Figure 1. Structure of RBFNN
Figure 2. The receiver constellation at BER = 0.49 Figure 3 and Figure 4 show that the system with BER = 0.5, 0.52 respectively are more accurate and better for equalizer learning curve with high performance of receiver constellation.
Figure 3. Receiver constellation at BER = 0.5 Optimization of transmission signal by artificial intelligent (Hassan Farhan Rashag)
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Figure 4. Equalizer learning curve at BER = 0.52 3.
CONCLUSION In this optimised method, the RBFNN is playing as an important tool for improved the communication system especially for transmission signal. The input signal is developed by activation function to minimize the distorted signal and to increase the response of transmission devices based on neurons and weight of hidden layer for RBFNN. The Simulink is built by tool box of matlab and the results is depended on difference values BER. finally, the system is better and optimized by high value of BER. REFERENCES [1] C. E. Shannon, “A mathematical theory of communication,” Bell System Techical Journal, vol. 27, pp. 379-423, 623-656, 1948. [2] E. Zehavi, “8-PSK trellis codes for a Rayleigh channel,” IEEE Trans. Commun., vol. 40, no. 5, pp. 873–884, 1992. [3] T. J. O’Shea, K. Karra, and T. C. Clancy, “Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention,” IEEE Int. Symp. Signal Process. Inform. Tech. (ISSPIT), pp. 223-228, 2016. [4] T. J. O’Shea and J. Hoydis, “An introduction to machine learning communications systems,” arXiv preprint arXiv:1702.00832, 2017. [5] D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck, “Deep learning for identifying metastatic breast cancer,” arXiv preprint arXiv:1606.05718, 2016. [6] D. George and E. A. Huerta, “Deep neural networks to enable real-time multimessenger astrophysics,” arXiv preprint arXiv:1701.00008, 2016. [7] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al., “Mastering the game of go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484-489, 2016. [8] M. Ibnkahla, “Applications of neural networks to digital communications: A survey,” Signal Process., vol. 80, no. 7, pp. 1185-1215, 2000. [9] M. Bkassiny, Y. Li, and S. K. Jayaweera, “A survey on machine-learning techniques in cognitive radios,” Commun. Surveys Tuts., vol. 15, no. 3, pp. 1136-1159, 2013. [10] M. Zorzi, A. Zanella, A. Testolin, M. D. F. De Grazia, and M. Zorzi, “Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence,” IEEE Access, vol. 3, pp. 1512-1530, 2015. [11] R. Al-Rfou, G. Alain, A. Almahairi, et al., “Theano: A python framework for fast computation of mathematical expressions,” arXiv preprint arXiv:1605.02688, 2016. [12] M. Abadi, et al., “TensorFlow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467, 2016. [Online]. Available: http://tensorflow.org
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International Journal of Advances in Applied Sciences (IJAAS) Vol. 8, No. 4, December 2019, pp. 293~306 ISSN: 2252-8814, DOI: 10.11591/ijaas.v8.i4.pp293-306
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Multi-objective wind farm layout optimization using evolutionary computations Chandra Shekar1, M. R. Shivakumar2 1 Department of ECE, JIT, India Department of EEE, SRSRIT, India
2
Article Info
ABSTRACT
Article history:
The usage of fossil fuels is actually not good for living nature and in future, this limited source of energy will vanish. Therefore, we need to go with the clean and renewable source of energy such as wind power, solar energy etc. In this paper, we are concentrating in wind power through optimizing the wind turbine placement in wind farm. The area-of-convex hull, maximize ‘output power’ and minimum spanning tree distance are our main objective topics, due to their effect in wind farm design. An implementation of modified version of the wind turbine (WT) placement model is uses to estimate the yields of the (wind farm) WF layouts and for simplifying the behavior of wind field, in this paper we use a simple wake approach. Moreover, to resolve the multi-objective problem here we proposed (Modified Genetic Algorithm) MGA, which is considerably better than the (Genetic Algorithm) GA and for evaluate the performance of MGA we use the multi-objective (EA) evolutionary algorithms such as; Genetic algorithm (GA) and SPEA2 and, produce different number of WT layouts. These methodologies are considered with various ‘problematic specific operators’ that are present in this paper.
Received May 3, 2019 Revised Jul 25, 2019 Accepted Sep 7, 2019 Keywords: Euclidean minimum spanning tree (EMST) Modified genetic algorithm (MGA) Wind farm Wind turbine
Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
Corresponding Author: Chandra Shekar, Department of ECE, JIT, Bangalore, India. Email: bmc.cbr@gmail.com
1.
INTRODUCTION The polluted environment is dangerous for humans’ health, which has increased many public concerns. In the current society, fossil fuel is the main source of energy, which are not ecological and will be exhaust in future because of rapid consumption, limited resources, global warming, climate changing, etc. [1]. Meanwhile, many ‘countries’ are trying to replace the fossil fuels by the use of renewable energy to make better environment. Like wind energy, safety, clean and higher rate of conversion are its main advantages as compared with the other renewable types of energy [1-3]. In the global market of renewable energy, increment of wind power is very much important. Global-cumulative production-capacity has predicted to increment of 791.9 Gigawatts by the end of 2020. Last year (2016), the cumulative-capacity growth-rate was 14.8% and in the present year, it forecast to achieve 13.7% of cumulative-capacity growthrate [4]. Figure 1 shows the Cumulative-market forecast by region 2016-2020.
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Figure 1. Cumulative market forecast by region 2016-2020 (source: Global Wind Energy Council) Optimization in placement of wind turbine is the method of determining the wind turbines (WTs) placement in WF (Wind Farms). In the objective, yield optimization of WT placements, the WTs should place within an offshore area ‘or’ specific land, so that we can achieve the maximization in output power. Wind farms are now an integral part of electricity generation grids, numerous such systems like IEEEReliability Test System (IEEE-RTS), West Denmark Power System (WDKPS). Here we will demonstrate the variant of multi-objective WT placement problem. The layout of wind farm is depend upon the “objective function”. The four different combination of objective has consider; convex hull area with the minimum spanning tree, output power (yield) with the convex hull area, output power with EMST, and a combination of yield, convex hull area and EMST. An implementation of modified version of the WT placement prototype [5] presented in [6] is uses to estimate yields of the WF arrangements. For simplify the behavior of wind field, in this paper we uses a “simple wake model”. We use the multi-objective (EA) evolutionary algorithms such as Genetic algorithm (GA) [7] SPEA2 [8] and, proposed algorithm M-GA (Modified Genetic Algorithm [9]), to produce different number of WT layouts. These methodologies are considered with various ‘problematic specific operators’ that are present in this paper. 2.
LITERATURE SURVEY A new algorithm MORS has proposed by Ju Feng et al. [10] that help to optimize the wind farm layout and the WT numbers. Generalized algorithm can be considered for multi-objective (> 2 objectives) and logically deal with the different constrains. Normally, a simple algorithm needs only fewer parameters, which are ease to implementation and run. Generality of the algorithm should be maintain by considering simple algorithm. Here they consider 2 objectives (minimizing cable length and maximizing power) with 2 constraints (WT proximity and WF boundary). After the evaluation with popular multi-objective EA (Evolutionary Algorithm) NSGA- II, it has found that ‘MORS’ (‘Multi-objective random search’) performs better than Non-dominated sorted GA II, mostly in case of ideal test. Furthermore, MORS has the additional dealing advantage with a variable WT number. Under the actual test case of Horn Rev 1 ‘WF’, MORS has shown the favorable performance. With the fixed number of wind turbines, that manages to get the shorter length cable and a little more production of power. The problem with variable number of WT’s, they got an extensive range of the Pareto-optimal layouts with the variable WT numbers. For developing, the WF MORS is quite useful for developers. That can be further test and improved in the future studies, which will help to considering more number of realistic objectives. Amin I et al. [9] proposed the model of NSGA-III, which outperformed the paternal algorithm interms of achieved solutions precision and the convergence. Increasing in objectives numbers, the Elite NSGA III corresponding performance significantly better than of NSGA-III in almost every instance. Furthermore, they influence on the exclusive population archive. The proposed algorithm capability in persevering elite population diverse sets and hence causes the improvement in offspring population diversity. For the further Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 293 – 306
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improvement, this study extends to investigate the elite-population archive impact, when the alternative recombination operators have used (e.g., (DE) differential evolution instead of the polynomial-mutationoperators and SBX). Secondly, investigation of elite-population archive impact when the neighboring solutions of elite has used to build offspring population. Lastly, consequently methodologies to avoid the build solutions has attracted towards some reference points. George Cristian et al. [11] proposed a WF layout optimization problem (WFLOP). The WFLOP objective is to determine the WT optimal placement within a WF. In this paper, they have focused on to maximize the power production. Wake effect is the major reason to significant losses of the power production; therefore, they used a discrete representation of WF. By using heuristic algorithms, the farm area is decomposed into cells set, where every cell having only one WT. In future, solution that is more exact and multiple optimized solution for the larger size problem is need. Markus Wagner et al. [12], focus on evolutionary based turbine placement with the enhanced wake fed prototype under real-world wind flow. They modeled real world geo-constraints data from the open Street Map. Moreover, the geo-constraints and realistic wakes modeling, the paper focus on comparison of the various approaches of evolutionary optimization. The proposed evolutionary strategies of four variants with the turbine-oriented operators and compared with the state-of-art optimizers. For the future work this can be extend to further scenarios of experimental analysis. In specific largescale, scenarios and offshore turbines placement with the ground and constraint like ship route. Important parts are constraints in realistic scenarios which, shown in result that the constraints produce problem in order to optimize the problem. It can be further concentrate on more developed constraint handling techniques. J. Day et al. [13] represent the effective and speedy algorithm for the huge type of wind farms layout enhancement. This consider problem specific feature and that can help to decrease the complexity of computation under considering Park Wake model. The result of that, algorithms achieves better quality of results than the existing approaches. The effective obtain speed is very less, in order of minutes ‘or’ hours instead of weeks or days. Parallelization approach can speed up the computation process and furthermore it can improve in future. Although here they consider a selected wake model (Park Wake model), and it is important that the optimized algorithm is very easy to applied to the different “Wake models” (such as the deep array 'wake model' [14]). Michele S. et al. [15] describes the “WFLOP” (wind farm layout optimization problem), which is critical problem that is necessary to solved during wind farm design. The better layout design tends to give a more profit and higher energy production. Only the several scientific communities have given consideration to such kind of constraints to this operational research area. In this, they have given the mathematical model that used to calculate the wake effect impact on production of energy. Main motive of the wind farm developer is to minimalize the road network, but the connection should be done properly. Such that one can reach, one point to any other point short of passing through the public roads. In that case, the cranes easily install the wind turbines and can easily move through the road network. This type of road network has built by subnetworks, which are separate from the public road. Initially, the ‘cranes’ need to move throughout the passenger road to reach next address. Unfortunately, the turbines are huge and the assembled one cannot transport through public roads. Therefore, it is necessary to disassemble, to transport over public road to another sub-network and, after that reassemble. Although, it is difficult to get information about operation cost (developers of WF do not publicize the cost information), it has estimated around tens thousands of dollars. W. Tong et al. [16] proposed a MOWFD (‘Multi-objective WF Design’) approach, which integrates and analyzes the several type of impact factors on the wind farms design. This methodology comes with three main advancement of WF design paradigm: first, one provides an understanding of key factor impact over performance of WF under the various wake model use. Second explores the important tradeoffs between COE (cost of energy), usage of area in WF layout optimization, and energy production. Third built an original advancement on the mixed-discrete PSO (particle swarm optimization) algorithm through concept of multi-domain diversity preservation for solving a difficult multi-objective optimization (‘MOO’) problem. A complete sensitivity analysis of WF power generation has performed to make understand and differentiate the land configuration impact, incoming speed of wind, installed capacity decisions and, ambient turbulence on conventional array layouts performance. Such that array WF, each factor are relatively important and vary considerably with the wake model choice, i.e., acceptable differences in sensitivity (up to 70%) were detect across the various wake models. Considering, optimized WF layouts, the selection of wake model has not much significant effect on the indices sensitivity. 3.
PROPOSED METHODOLOGIES In the following, the different constraints and objectives are outline, which we consider for optimization of wind farm. Let 𝐴 = { 𝐴 , . . . , 𝐴 } and 𝐵 = { 𝐵 , . . . , 𝐵 } are set of the coordinates 𝑥 and 𝑦 of 𝑛 WT in the plane. Our objective is to obtain such coordinates set that the whole Multi-objective wind farm layout optimization using evolutionary computations (Chandra Shekar)
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WF output is maximize. At same time, the whole cable length essential to inter-connect the turbines, also the necessary area for the WF, which should be minimum. Moreover, the layout developer has to pay certain attention towards constraints. Every aforesaid combined objective is set to solution as distinct objectives. 3.1. Energy output The overall output energy of the WF varies, depending upon the selected coordinates, because here we considering wake effects into the account. In reference [17], it has already declared that an efficiency of wind turbine would be decrease after putting into a WF with other turbine, because of wake effect. Wind flows over a WT; the kinetic energy part has transferred to turbine blades. Because of that wind speed is reduce by the blades, it generates an expansion of volumetric regarding to the mass accumulation earlier the blades. Wake model can be simplify by without considering the intensity of nearest turbulence. That effect has assumed to propagate linearly and continuously as shown in Figure 2. Increment in wake effect when more wakes have apply to same WT. The analytical wake effect model is consider in this paper that was 1st developed by the Jensen [18] is known as park wake model.
Figure 2. Schematic of a wake model involving wind turbines with different hub heights The Park wake prototype, it is a trade-off between the computationally very expensive simulations and simplifying wake models, which has based upon the dynamics of fluid. The effects of wake upon a turbine 𝑛 has change the available wind resource to another direction, through decreasing the scale parameter 𝑝 of ‘Weibull Distribution’ predicted for the whole farm that called as free-stream ‘wind resource’. This wind resource has reliant on its address and rest turbines location. Through, we have a 𝑝 scale parameter for every turbine 𝑛: it is having complex computation and involves 𝑆𝑑𝑒𝑓 velocity deficits, which the 𝑛 turbine experiences the influence due to other. Algorithm 1: Wake effects evaluation procedure (“Park model”) [19, 20] a. Providing {𝐴, 𝐵} as the locations and thrust-coefficient 𝐾 is given for the WT, wake spreading factor 𝑠 that is landscape-specific and, rotor diameter 𝐷; b. 𝑡 = 1 − 1 − 𝐾 , 𝑏 = 𝑠 /𝐷, 𝑢 = unit step function; 𝑚 = (𝐵 − 𝐵 )𝑠𝑖𝑛𝜃 + (𝐴 − 𝐴 )𝑐𝑜𝑠𝜃 ; c. 𝑑 , = ‖𝑚‖, 𝑧 = tan 𝑠 ; d. for 𝑛 = 1 to turbines number perform e. for 𝜃 = 0 𝑡𝑜 360 perform f. for 𝑎 = 1 to 𝑥 − 1; also 𝑎 ≠ 𝑛 perform g.
𝛿
h.
𝑆𝑑𝑒𝑓(
,
= cos , )
;
=𝑢 𝛿
,
− 𝑧
; ,
i.
𝑆𝑑𝑒𝑓 =
j.
𝑝 (𝜃) = 𝑝 (𝜃) × (1 − 𝑆𝑑𝑒𝑓 );
∑ 𝑆𝑑𝑒𝑓(
, )
;
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Turbines 𝑛, 𝑎 ≠ 𝑛 (see Algorithm 1). We refer to [19] for the detailed demonstration on a parameter computation, since the “wake effects” present in the Park wake effect approach. In terms, the predictable energy output 𝜂 of all WF is given thru the (1). 𝑇
[𝜂] = ∑ ∫ 𝐻(𝜃) ∫ 𝐻(𝑦(𝜃) , 𝑝 (𝜃, 𝐴, 𝐵), 𝑠(𝜃) 𝛽 (𝑦).
(1)
Here, 𝑦 is ‘wind speed’, and the function 𝛽 (𝑦) describes the ‘𝑝𝑜𝑤𝑒𝑟 𝑐𝑢𝑟𝑣𝑒’ for 𝑛 turbine. However, Wind speed 𝑦 is random variable as per a ‘Weibull distribution’. Which has projected from resource data wind and, using A and B considers as the ‘wake effect’. That type of distribution is a wind direction function, in which 𝜃 varies from the 0 − 360 . Here the wake effects is not disturbing the Weibull distribution. Moreover, a flow of wind from a specific direction having some probability 𝐻(𝜃). 3.1.1. Constraints Here we are following some constraints that placed on our “optimization function”. The upper bound WF area has taken as the first constraint. This ensures that a turbine 𝑛 place within a “certain area”. The length 𝐹 and width 𝐹 for a rectangular wind farm, this constraint should satisfy. 0 ≤ 𝐴 ≤ 𝐹 𝑎𝑛𝑑 0 ≤ 𝐵 ≤ 𝐹 , 1 ≤ 𝑛 ≤ 𝑥. The damage risk increase through turbulences, when turbines placed too close. Spatial proximity is the second constraint. The equation is given as (𝐴 − 𝐴 ) + (𝐵 − 𝐵 ) ≥ 𝑢 ∙ 𝐷, 1 ≤ 𝑛 ≠ 𝑎 ≤ 𝑥 Here, 𝑢 is a ‘proximity factor’ and 𝐷 is a ‘rotor radius’. We consider 𝑢 = 8 as per the industry standard. 3.2. Euclidean Minimum Spanning Tree (EMST) The EMST has used to compute a minimal cable length necessary to link the all WTs in a particular WF layout. It can calculated by the first complete construction graph that represent the WT set of points. Edge costs has calculated by Euclidean distance between the any turbines pair. For this graph minimal spanning tree distance is calculated and used as a main objective, which representing the cable length costs. Figure 3 displays a layout of wind turbine, and minimal spanning tree, which has denoted by lines joining to each WT. Shown turbines has enclosed thru a ‘rectangular area’, which presented by grey color. This theoretical EMST is calculated using formula 22 in [19] equation as 𝐿
= (𝑥 − 1) × 38.5 × 8.0
(2)
Where, the Turbine RR (Rotor Radius) proximity constraint is 38.5 m and n is the number of turbines in the wind farm. The area enclosed by dash line is represents convex hull area. The joining lines shows the edges of EMST, or cables. The drawn circles visualize the least safety distance imposed by the proximity constraint (twice the rotor radius of turbine).
Figure 3. An example wind turbine layout Multi-objective wind farm layout optimization using evolutionary computations (Chandra Shekar)
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3.3. Area of convex hull Area that cover all the set of points is known as area of convex hull. The occupied area is the minimal land area, which has needed for an optimal WF layout. Here convex hull area can be computed by the Graham’s scan model [20]. Figure 3 displays a layout of WT, and the convex hull area (cost) enclosed by dash grey line. If the outline is ‘non-intersecting polygon’ with x vertices, then the area of convex hull can be compute. 𝐴
= ∑
(𝐴 𝐵 + 1 − 𝐴 + 𝐵 )
(3)
3.4. Variation operators for the turbine placement In this section, the multi-objective WT placement framework problems and problem-specificvariation operators are outline. Because of the large turbines number, the problem is more constrained. Therefore, the operator has to confirm the feasible placements of turbines. 3.4.1. Movement mutation Local changes of current solution is done by the mutation operator, i.e. turbines placement on the given land area. The ‘mutation operator’ (i.e Movement Mutation Algorithm 2) randomly selects turbine moves in a particular proportion of specified solution. Subject to constrains, the movement direction and distance is determined randomly. Algorithm (2): Mutation operator (i.e. Movement Mutation) a. The 𝑆 selected as in with turbine with locations {𝐴, 𝐵}; b. Consider turbine location 𝑄 = {𝑥, 𝑦} is chosen, 𝑄 ∈ 𝐶, 𝑥 ∈ 𝐴 𝑎𝑛𝑑 𝑦 ∈ 𝐵 ; c. Here, 𝑆 = protection distance; d. Set 𝑅 is ‘movement range’ in horizontal direction of 𝑄 and 𝑅 is the movement range’ in vertical direction of 𝑄; e. for each 𝑄 ′ = {x′, y′} : {y − 𝑆 < y′ < y + 𝑆 } f. if x′ is near to x on nonnegative way; g. Perform h. Save x′ to 𝑥 ; i. if x′ is near to x on non-positive direction; j. Perform k. Save x′ to 𝑥 ; l. 𝑅 varies from 𝑥 − 𝑆 to 𝑥 + 𝑆 ; m. Re-do the steps v to xii to get the 𝑅 ; n. Return 𝑄 = {𝑥 , 𝑦}: (𝑥 ∈ 𝑅 ) 𝑜𝑟 𝑄 = {𝑥, 𝑦 } ∶ (𝑦 ∈ 𝑅 ); In order to get the feasible solution, the max distance that WT can move in every (each) has computed. This can be done thru examine a designated surrounded area, with movement of x-axis and y-axis. In the checkup area, WT whose safety rotor margins has found to be overlap, limit the movement constraints for particular turbine. The defined designated area is two times of 38.5-meter safety margin, originating each direction perpendicular to the travel axis. To get valuable placement after the turbines movements, 𝑅 and 𝑅 (movement ranges) has computed, which has based on the placement of neighboring turbines. Afterwards, the MM (Movement Mutation) approach not give an infeasible individual child and algorithm 2 shows the MM operation algorithm, which is based on the simple single loop calculation. 3.4.2. Block swap crossover Block Swap Crossover (BSC) has considered to produce the two children from randomly selected two parents from each block, each child having varying information from their parents. A wind farm is a rectangular area block, entire wind farm enclosed by the boundary to provide protection [20]. The first parent basis is uses by the first child. A randomly block has chosen from 2 nd parent to copying 1st child, consequently, the boundaries of the block has extended afar the proximity constraint of each turbines through the twice rotor radius value (i.e. 38.5 meters). This will help to get a safety distance between turbines that are outside of these circle bounds. Algorithm 3: ‘BSC’ (Block Swap Crossover) a. Consider 𝑆 and 𝑆 are chosen as parents, with in the turbine locations; Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 293 – 306
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for 𝑛 = 0 to T Rectangular area 𝐴 is generated randomly 𝑎 in WF area; Turbines in 𝑆 are separated into two sets, Ι for turbines inside𝐴 and Θ for ones outside of 𝐴 ; Modify 𝐴 to 𝐴 in that every edge keeps a protected space to the ‘adjacent turbine’ in Ι ; Considering 𝐴 on 𝑆 to get Ι and Θ ; if |Ι | ≥ |Ι | then Move x = |Ι | − |Ι | turbines from Θ into Ι ; Set the turbine locations in Ι as in Ι ; Return 𝑆 as child number one; Else Process continue; Provide above on the 𝑆 , to obtain the 2nd child number;
Then the child boundary is used to get the area, which is totally replaced through the turbine 2𝑛𝑑 parent. This method has continued for the operation of 2nd child, which is basis on information of second parent. The process of copying turbine position is straightforward, in which the child holds equal or lesser turbines related to their parents has replaced at in destination area. A newly boundary is selected randomly until the case condition is not satisfy. Even if the appropriate block has not obtain for the copying child, then its parent will appear as the operation result. This thing indicates the failure in crossover operation. If destination turbines number are matches with the source area, the operation ends. Then the randomly selected child destination area from outside has to be moved into destination area like as substitute. Which confirms the turbine number is equal in both destination and source. 4.
PROPOSED METHODOLOGIES There are many proposed Genetic Algorithm (GA), but the basic framework remains same as the original GA [7] with some significant alterations in its “selection mechanism”. According from G.C Ciro. [21] it is found that the ‘MGA’ has performed superior than the GA, if the problem is multi-objective. Adoption of MGA is to solve problems, which more than two ‘objective’ due to the classification of solution with some reference point and selection of best qualified for ‘next population’. Through altering some selection mechanism here, we proposed an MGA. The steps define in algorithm 4 describes the MGA. Algorithm 4: Modified Genetic Algorithm (MGA) a. Reference point number is calculate to place on a ‘ℎ𝑦𝑝𝑒𝑟 − 𝑝𝑙𝑎𝑛𝑒’ b. Random generation of initial population with considering the resources assignment constraints. c. Recognize the sorting of non-dominated population d. For 𝑡ℎ𝑒 𝑛 = 1 𝑠𝑡𝑜𝑝𝑝𝑖𝑛𝑔 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑎 do e. Using tournament method select the two parents 𝑆 and 𝑆 , f. The crossover is apply between 𝑆 and 𝑆 ,with a 𝑝(𝑐) probability, g. Recognize the sorting of non-dominated population, h. Normalize the ′𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑚𝑒𝑚𝑏𝑒𝑟𝑠′, i. Associate the reference points with the population member, j. Apply the (𝑐𝑜𝑢𝑛𝑡𝑒𝑟) niche preservation k. Keep the obtained niche solution for the “𝑛𝑒𝑥𝑡 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛” l. End for. 4.1. Reference points determination on a hyper-plane We must have to define a reference points set to confirm the obtained solution diversity. On the standardized hyper-plane, the different points set are place, which have the identical orientation in all axis. The reference point’s number (𝐺) defined as 𝐺=
𝑒+ 𝑁 −1 𝑁
(4)
Where 𝑒 is number of the objective function and 𝑁 is divisions number, which consider on each objective axis. After the placement of reference point, the created reference points is consider to associate the solution. Multi-objective wind farm layout optimization using evolutionary computations (Chandra Shekar)
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4.2. Population members normalization The ideal point must be determined from the current population, so that we identify the minimum objective value of each function. In order to create the hyper-plan, we are following the steps, which has proposed by Jain and Deb et al. [22]. 4.3. Associate the reference points with the population member When each objective function has normalized, it is essential to associate every population member with the reference. Reference line is defined for joining the point with initial point. Afterwards, perpendicular distance between each reference line and each population member is define. Lastly, the point of reference that are close to reference line from the individual population has allowed being the population member. 4.4. Niche preservation operation A reference point set can be relate to the one ‘or’ more members of solution, but here we take that solution which is closer to point (the perpendicular distance from origin line [22]) 4.5. Genetic operators The generation of children is the same operation, which were also using in genetic algorithm. According to Jain and Deb [22], we have fixed the size of population (𝑆𝑂𝑃) close to a reference point’s number (𝐺) to provide the importance to every population member. 5.
RESULT ANALYSIS In this paper, we consider the variation operator and multi-objective EAs (evolutionary algorithms) to solve the multi-objective WT placement problems. The 3-multi-objective EAs are consider such as; MGA [9], SPEA2 [8] and GA [7], and the execution has done in the ‘𝑗𝑀𝑒𝑡𝑎𝑙 framework’ [4]. The simulation has done using Matlab 2016b, the system configurations is 8 GB RAM, Windows 10 and Intel i5 processor. To evaluate the performance of our proposed approach we used wind-Scenario-2 [23]. Scenario 2 can be consider as complex scenario and the prevailing direction of wind covers a sector broad about 105◦. There are two cases, first when wind is coming from only one direction and second when wind is coming from different directions. As per intensity of wind, the direction has consider thru “Weibull distributions” at both considered cases, which results there is non-zero probability for the second case. Hence, the layout optimization is necessary to work within the minimal wake loss with all wind direction. Finally, the turbine rotor radius 𝐷 = 38.5𝑚 is use for proximity constraints. In wind scenario, here we are computing the yield (power) that can be used as an main objective to our WF optimization. The convex hull area and EMST are the other objectives, which we are considering to minimize the cost of wind farm. In our experiment, we used the multi-objective GA, MGA, and SPEA2 optimization algorithms with our proposed operators. In every instance of simulation, 50-population size has utilized with over 10,000 generations. The MM operator has applied with 𝑝(𝑚) = 0.7 probability in the individual iteration. Similarly, the block swap (BS) ‘crossover operator’ has provided with 𝑝(𝑐) = 0.3 probability (based upon the initial testing both value were selected/chosen). The following objective combinations are: a. Maximizing the yield and, minimizing the ‘area of convex-hull’ b. Maximizing the yield and, minimizing the EMST c. Minimizing the area of the ‘convex hull’ and, EMST d. Also the combination of yield, convex hull area and, the EMST. For analyzing the better performance of evolutionary algorithms, here we taken the three scenarios of turbines. In first scenario, we took the four turbines for placement on the given area (Area size = 3000×3000 𝑚 ). In second scenario, we took the twenty turbines for placement on same area. In last case, we took thirty turbines at same given area. Considering these following objective mixtures with the variation operators, the enhanced location of wind turbine can be obtained. 5.1. Scenario one for wind turbines placement The placement of wind turbines is critical task. As we earlier mentioned that in scenario one we are taking 4-turbines to place on rectangular land area (9 𝐾𝑚 ). In according to the variation operator and objective combination the placement of wind turbines are carried out. Combinational objective has shown in Figure 4, which has combination of all three main objectives (yield, convex hull area and, the EMST). X-axis represent the area in 𝑚 , Y-axis represent the yield in kW and Z-axis represent for EMST in meter. In Figure 6, three different color represent for three different evolutionary algorithms. Blue color represents for M-GA, green color for GA, and red color for SPEA2. Int. J. of Adv. in Appl. Sci. Vol. 8, No. 4, December 2019: 293 – 306
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Through using these combinational objectives, we got our turbine placement location. The circle shows the minimal distance of safety that has imposed by proximity constraint. Figure 5 shows the 4-turbines placement using M-GA, Figure 6 shows the four turbines placement using GA algorithm, and Figure 7 shows for turbine placement using 𝑆𝑃𝐸𝐴 − 2. In each Figure 5 to Figure 7 the turbine placement is different, which causes the differences in objectives values.
Figure 4. Turbine-4, a combination of power, convex hull area and minimum spanning tree
Figure 5. Turbine-4 placement using MGA
Figure 6. Turbine-4 placement using GA
Figure 7. Turbine-4 placement using SPEA2 Multi-objective wind farm layout optimization using evolutionary computations (Chandra Shekar)
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Figure 8 shows the presentation of Yield (kW), Area of convex hull (𝑚 ) and EMST (𝑚) using evolutionary algorithms 𝑀 − 𝐺𝐴, 𝐺𝐴 and 𝑆𝑃𝐸𝐴 − 2. The presented results show that M-GA of 0.04 𝐾𝑚 occupying 66.93% less area when compared to GA (0.11 𝐾𝑚 ) and 27.72% less area compared to SPEA-2 (0.05 𝐾𝑚 ). A 0.2% more yield produced by M-GA (29.22 𝑀𝑊) compared to SPEA-2 (29.17 𝑀𝑊) and 0.08% more yield compared to the GA (29.20 𝑀𝑊). The minimal spanning tree distance is also the important factor in which SPEA-2 (1.46 𝐾𝑚) doing well in lesser number of turbines. SPEA-2 require 13.2% less cables to connect all the WT compared to M-GA (1.68 𝐾𝑚) and 14.85% less cable length compared to GA (1.71 𝐾𝑚) is reported.
Figure 8. Turbines-4, representation of Yield (kW), Area of convex hull (m^2) and EMST (m). Using evolutionary algorithms MGA, GA and SPEA2 5.2. Scenario two for wind turbines placement In scenario two, we are taking twenty turbines; same as the previous scenario here we are following same procedure to get optimized result from WF. A combination objective of power, EMST and convex hull area is shown in Figure 9. For every population (population size =50) the objectives values are plotted. From Figure 9, we can say that GA and SPEA-2 having more variation in every population with compared to MGA. That combinational objective will help to place the turbines effectively. Therefore, we should get maximum power output from the wind farm, with lesser, EMST and convex hull area. The placement of 20 turbines has shown in Figure 10 to Figure 12 with using of different evolutionary algorithms. Location of turbines from algorithm MGA, GA and SPEA-2 are different from each other, which means the area covered by the turbines are not the same. The area given to place turbines is 9 𝑘𝑚 , which is a rectangular area. More area in wind farm indicates the more initializing cost. A less convex hull area is always desired objective parameter and this parameter considered in our paper for optimization. The representation of convex hull area, EMST and yield is shown in Figure 13 for the 20 turbines. The presented result exhibit 𝐴 >1.4𝐾𝑚 considering M-GA, SPEA-2 and GA models. The more area is achieved by GA, which is 10.74% more compared to M-GA model of 1.48 𝑘𝑚 . SPEA-2 model occupying area of 1.49𝐾𝑚 that is 0.74% more 𝐴 compared to M-GA model. The highest yield is achieved by the MGA of 127 MW that is 1.42% more compared to SPEA-2 model of 125 MW. A 0.19% more yield is obtained by M-GA compared to the GA. The increment in turbine numbers and area of convex hull will affect on EMST ( 𝐿 ) values. As per the obtained result, the maximum length is obtained by SPEA-2 of 6.2 Km, which is 3.65% more compared to M-GA. The minimum EMST obtained by the M-GA of 5.9 Km that is 3.33% less than GA of 6.1 Km is reported.
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Figure 9. Turbine-20, a combination of power, convex hull area and minimum spanning tree
Figure 10. Turbine-20 placement using MGA
Figure 11. Turbine-20 placement using GA
Figure 12. Turbine-20 placement using SPEA2
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Figure 13. Turbines-20, representation of Yield (kW), Area of convex hull (m^2) and EMST (m). Using evolutionary algorithms MGA, GA and SPEA2 5.3. Scenario three for wind turbines placement Same as the other two scenario here we showing the combinational objective of thirty turbines in Figure 14. Turbines placement using M-GA is shown in Figure 15, in which the blue dot represent the turbines and circle around that blue dot represent the safety distance between the turbines in wind farm. 30 Turbines placement using the GA is shown in Figure 16 and Figure 17 show the turbines placement using SPEA-2 model. The turbines placement has done randomly by the variation operators. The performance of all wind turbines model is evaluated on basis of optimized objective parameters.
Figure 14. Turbine-30, a combination of power, convex hull area and minimum spanning tree
Figure 15. Turbine-30 placement using MGA
Figure 16. Turbine-30 placement using GA
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Figure 17. Turbine-30 placement using SPEA2 Figure 18 shows the presentation of Yield (kW), Area of convex hull (𝑚 ) and EMST (m) using evolutionary algorithms 𝑀 − 𝐺𝐴, 𝐺𝐴 and 𝑆𝑃𝐸𝐴 − 2. The result presented exhibit more convex hull area (𝐴 > 2.30𝐾𝑚 ) considering M-GA, GA and SPEA-2 models. The lowest 𝐴 achieved in M-GA model of 2.30𝐾𝑚 , which is 3.9% less compared to GA (𝐴 = 2.39𝐾𝑚 ) and 6.52% less compared to SPEA-2 model of 2.46 𝐾𝑚 . The result exhibits more yield > 180 MW considering evolutionary algorithm models [8-10]. A 0.94% more yield is produced by M-GA of 182 𝑀𝑊 compared to GA model (180.6 𝑀𝑊) and 1.06% more yield compared to the SPEA-2 model (180.4 𝑀𝑊). As increasing in turbine numbers, the cable length will also increase. The obtained result exhibits 𝐿 < 9.3 Km considering SPEA-2, 𝐺𝐴 and MGA model. Considering all the algorithm models minimum 𝐿 is obtained by M-GA of 9.04 Km, which is 1.22% less compared to the GA model ( 𝐿 = 9.15 𝐾𝑚) and 1.75% lesser than the SPEA-2 model (𝐿 = 9.15 𝐾𝑚). This means M-GA exhibits better performance in all optimized objective parameter is very efficient for every scenarios of WF.
Figure 18. Turbines-30, representation of Yield (kW), Area of convex hull (m^2) and EMST (m). Using evolutionary algorithms MGA, GA and SPEA2 6.
CONCLUSION In this paper, we are concentrating in wind power through optimizing the wind turbine placement in wind farm. In proposed wind scenario, we computed the yield (power) that has used as one of the main objective in our WF optimization. The convex hull area and EMST are the other objectives, which we are considering to minimize the cost of wind farm. The multi-objective GA and, SPEA2 optimization algorithms has compared with our proposed MGA for analyzing the better performance of evolutionary algorithms, here we taken the three scenarios of turbines. In first scenario, we took the four turbines for placement on the given area (Area size= 3000×3000 𝑚 ). In second scenario, we took the twenty turbines for placement on same area. In last case, we took thirty turbines at same given area. At each scenario, the turbines placement is Multi-objective wind farm layout optimization using evolutionary computations (Chandra Shekar)
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shown in the given area; also, we provide the representation of Yield (kW), Area of convex hull (m2) and EMST (m) using evolutionary algorithms MGA, GA and SPEA2. Taking average from three scenarios, proposed MGA produce 0.89% more yield than SPEA2 and 0.403% more yield than GA, which is considerable improvement in WF optimization process. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]
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