International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN 2250-155X Vol. 3, Issue 2, Jun 2013, 261-276 © TJPRC Pvt. Ltd.

ON-LINE MONITORING OF POWER SYSTEM PARAMETERS USING RADIAL BASIS FUNCTION NEURAL NETWORKS P. GOPI KRISHNA1, T. GOWRI MANOHAR2 & FARIS AL-NAMIY3 1

Lecturer, Engineering Department, IBRA College of Technology, Ibra, Oman

2

Associate Professor, Department of EEE, S. V. University College of Engineering, Tirupathi, India 3

Head of Engineering Department, IBRA College of Technology, Ibra, Oman

ABSTRACT This paper proposes a practically implemented on-line monitoring of an equivalent 2-bus power system parameters and a simulation based 72-bus Indian southern power grid parameters. The on-line monitoring of various power system parameters such as power flow, power loss, voltage magnitudes and phase angles is possible by the proposed method which mainly uses Arduino PCBs with Atmega-168 microcontroller works with arduino1.0.3 software and Radial Basis Function Neural Network (RBFNN). The proposed method revealed that the continuous monitoring of power system parameters is possible by means of intelligent techniques not by conventional load flow methods, as these methods involve iterations towards the convergent point. The results presented in this paper are obtained through the proposed method, RBFNN and Repeated Power Flow (RPF) as a conventional method which uses the Newton Raphson’s method in polar coordinates. The results are obtained at different loading conditions, at constant power factor and the comparative results between RPF, RBFNN have negligible error. Hence the results obtained through proposed method are very useful for the power engineer at different load dispatch centres for making quick decisions in effective operation, maintenance of existing power system. In this way, the proposed method has been proved as the advancement in engineering research apart from the existing methods of usage of conventional methods in the on-line monitoring of power system parameters.

KEYWORDS: Power Flow, Voltage Stability, Arduino, Atmega Microcontrollers, Neural Networks etc., INTRODUCTION The expansion of power system is increasing with the demand of modernization of society across the world. At the same time, the existing controlling techniques and analysis methods based on the conventional techniques like Repeated Power Flow (RPF) [4] which uses Newton Raphson’s method cannot fulfil the necessary requirements related to planning, maintenance and smooth operation of the power system. Even though these methods give the base information related to the steady state operation of power system, they fail to give the on-line information, due to the involvement of iteration steps towards the convergent point. These drawbacks would be overcome by on-line monitoring techniques, which are the most useful techniques to obtain precise and necessary information about any power system equipment, substations and problems related to different power system. The time to time (on-line) monitoring of power system parameters will plays a vital role in modern power systems, as these are interconnected by transmission lines to transmit the large amounts of electric power over thousands of kilometre for both reliability and economic reasons and also in the aspect of uninterruptable electricity supply with high quality constitutes one of the most important requirements for the development of human societies. The power system operation has been effectively monitored by different techniques, such as wired monitoring of power system using Supervisory Control and Data Acquisition (SCADA), web based monitoring, digital data recorders [1] and wireless

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monitoring of power system using the latest Phasor Measurement Units (PMUs). These techniques have been used at various load dispatch centers for monitoring and controlling of the power system transmission lines, substations, power equipment. Power systems state monitoring act as an important role in system operation, online decision making and as the basis for the real time power flow analysis. Conventionally, the power system states like voltage magnitudes, voltage phase angles, current magnitudes, power flows on transmission lines at different busses were measured using analog devices such as current transformers, potential transformers. These states are communicated to the energy management system (EMS) through the supervisory control and data acquisition (SCADA) system [2]. System information is intermittently updated with the sampling frequency in the range of a few seconds. State estimation is then performed to acquire a converged solution for further application. This approach served the power system well, but it lacked the ability of observing measurements across the whole system because the data was not time synchronized. So when using these systems there is no way of obtaining a real time snapshot of the system, but the advent of synchronized phasor measurements has revolutionized the field of power system state monitoring. PMUs have significant advantages over the traditional measurements in terms of both accuracy and speed of measurement by utilizing the global positioning system (GPS) receivers and microprocessors. The time-stamped digital phasors calculated in the PMUs are synchronized to a common time frame by satellites and assembled into a series of data streams for communication to remote control centers [3]. The Radial Basis Function Neural Network (RBFNN) is proved as the best non-linear function approximation technique [4] , as it can model any multi input and multi output non-linear system easily with less numbers of iterations, during the pattern reorganization at the time of its training. Therefore, in this paper the RBFNN is used as the main contribution in the proposed equivalent 2-bus system as an intelligent monitoring of on-line parameters. The same concept will be applicable to any two buses in the considered 72-bus, Indian southern power grid and it can be extended to all buses. In general, the data obtained from the different buses and equipments through the on-line monitoring methods will be given to intelligent systems at various load dispatch centers [5] for processing information, monitoring the obtained data in the computer and to acquire precise solutions to different issues in the analysis of power system, for example load forecasting, planning, maintenance, making quick decisions in the case of emergency conditions. This is called as the intelligent monitoring. The Arduino printed circuit boards (PCBs) with Atmega168 microcontroller is the key hardware used in the proposed system to detect continuously sending end power and receiving end power. The Atmega168 microcontroller is programmed to compute power loss between sending end bus and receiving end bus and at the same time it is giving sending end power as an input to the trained RBFNN, which is trained with the training data obtained on 72-bus Indian Southern Power Grid. Its accuracy is verified with the different testing patterns and compared with the conventional RPF method, the results are presented in the test system and results section. The brief details of mathematical modelling RPF method, Arduino hardware details and its operation, RBFNN architecture with its training algorithm, 2-bus equivalent and 72-bus Indian southern power grid test systems and their comparative results are presented in the following sections.

REPEATED POWER FLOW (RPF) The Repeated Power Flow (RPF) method [1], solves repeatedly power flow equations (3) and (4) with specified load increments, with a constant power factor and is used for finding the voltage magnitudes, phase angles, power flows

263

On-Line Monitoring of Power System Parameters Using Radial Basis Function Neural Networks

and power losses at various buses for the considered 72-bus Indian southern power grid system. The RPF uses the Newton Raphson’s method in polar co-ordinates. Newton Raphson’s Power Flow Solution The complex power at bus ‘i’ is

Pi jQ i Vi * I i

(1) n

Pi jQ i Vi * i Yij V j ( ij j )

(2)

j 1

Separating the real and imaginary parts, the real and reactive power flow equations are obtained as n

Pi Vi Yij V j cos( ij i j )

(3)

j 1

n

Qi Vi Yij V j sin( ij i j )

(4)

j 1

The equations (3) and (4) represent a set of non linear algebraic power flow equations in terms of the independent variables, voltage magnitudes per unit, and phase angle in radians. Expanding these two equations in Taylor series about the initial estimator and neglecting all the higher order terms results in the set of linear equations in the form of jacobian matrix. The jacobian matrix at the kth iteration is obtained using equation (5).

(5) In the equation (5), bus-1 is assumed as the slack bus. This jacobian matrix gives the linearized relationship between small changes in the voltage angles

i

K

and voltage magnitudes Vi

(K )

with small changes ‘i’ real and

reactive powers. Elements of the jacobian matrix are the partial derivatives of equations (3) and (4), evaluated at

i

K

and Vi

P J1 Q J 3

(K )

respectively. In short form this can be written as

J 2 J 4 V

(6)

The diagonal elements of J1, J2, J3 and J4 are determined using equation (7).

Pi Qi Pi Qi , , & i Vi i Vi

(7)

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Similarly for the off-diagonal elements in the place of i, j is used in the denominator terms of the above diagonal

Pi

elements. The terms

(K )

and

Qi

(K )

are the difference between the scheduled and calculated values, known as the real

power and reactive power residuals, these are given by equations (8) and (9).

Pi

(K )

Qi

Pi

(K )

( sch)

Qi

Pi

( sch)

(K )

Qi

(8)

(K )

(9)

While solving repeated power flow using Newton Raphson’s method in polar co-ordinates, when

Qi

(K )

Pi

(K )

and

are less than specified tolerance limit, then the new estimates of bus voltages and corresponding voltage angles

are obtained by equations (10) and (11) respectively.

Vi

( K 1)

Vi

(K )

Vi

(K )

(10)

i ( K 1) i ( K ) i ( K )

(11)

RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN) The architecture of Radial Basis Function Neural Network (RBFNN) [9] [6], is shown in Figure (1), which consists of input layer, hidden layer and output layer. The Radial Basis Function (RBF) represented in equation (12) is used as an non-linear activation function for the hidden layer neurons and output layer neurons in the RBFNN. n

i exp(

(x i 1

Where,

i

wij ) 2 )

2 2

(12)

i is the hidden neuron’s activation function, x i is the input vector, wij is the connection weight between

ith neuron and jth neuron and

is the spread of RBF. The RBFNN has become increasing popular [10] because it is one of

the best function approximation intelligent techniques [2] and is used for modelling of any type of application related to any field.

Figure 1: Architecture of Radial Basis Function Neural Network To accomplish the best training efficiency from RBFNN, the prerequisites[16][17] are the training data and checking data (data patterns) these are obtained from the simulation results of test system-1 using repeated power flow, presented in the test systems and results section of this paper.

On-Line Monitoring of Power System Parameters Using Radial Basis Function Neural Networks

265

ARDUINO BOARDS AND SOFTWARE The on-line monitoring of power systems parameters are obtained practically using Arduino boards by integrating with RBFNN. The Arduino board consists of ATMEGA168 microcontroller; it is a small computer on a single integrated circuit consists of a processor core, memory and programmable input/output peripherals. What is Arduino? Arduino with ATMEGA168 microcontroller [15] with flash memory 32KB, EEPROM 1KB, SRAM 2KB, clock speed 16MHz acts as s a tool for making computers that can sense and control the hardware. The microcontroller is programmed using the Arduino programming language based C or C++. As a first step the required program is verified by compiling the program using Arduino 1.0.3 software, after that this program is uploaded into ATMEGA168 microcontroller. Arduino 1.0.3 software is an open-source based on a simple Input/output board and a development environment that implements the processing/wiring language. Arduino can be used to develop stand-alone interactive objects or it can be connected to software on computer.

TEST SYSTEMS AND RESULTS Test System-1: Indian Southern Region Extra High Voltage (SREHV) 72-Bus System [7] The SREHV 72-bus power system is divided into three areas ZONE-1, ZONE-2 and ZONE-3shown in figure

Buses 2,3,4,5,16,24,25,30,31,33,35,36,44,45,54,56,57,58 are in ZONE-1.

Buses 1,6,7,8,17,18,19,20,21,22,23,26,27,32,46,47,52,55,59,60,61,62,63,66,69,70 are in ZONE-2.

Buses9,10,11,12,13,14,15,28,29,34,37,38,39,40,41,42,43,48,49,50,51,53,64,65,67,6871,72 are in ZONE-3.

Buses 1 to 15 are generator buses.

Bus16,17,18,19,20,21,22,23,25,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,48,49,52,53,54,55,64,65,66,68, 69,71,72 are the load buses. The remaining buses are dummy buses and are used for interconnection.

Figure 2: Single Line Diagram of Southern Grid 72-bus E.H.V System The location of each bus of test system-1 is identified in 72-Bus Southern region power map of Indian system [8] as shown in Figure 3.

266

P. Gopi Krishna, T. Gowri Manohar & Faris Al-Namiy

Figure 3: 72-Bus Southern Region Power Map of Indian System

CASE SCENARIOS FOR TEST SYSTEM-1 The following four cases are considered to verify the working and accuracy of the RBFNN and to compare with the RPF method. Case 1: Varying the load at BUS-17

Case 2: Varying the load at BUS-21

Case 3: Varying the load at BUS-46

Case 4: Varying the load at BUS-7

Case 1: Varying the Load at Bus-17 The Repeated Power Flow(RPF) using Newton Raphson method in polar co-ordination and intelligent technique using RBFNN are used on bus-17 of SREHV 72-bus system by increasing load in steps , with constant power factor and the load at other buses are kept as constant in all steps. The corresponding voltage magnitude and phase angles are shown in Table 1.

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On-Line Monitoring of Power System Parameters Using Radial Basis Function Neural Networks

Table 1: Varying the Load at Bus-17 Real Power At Bus-17 (MW)

RPF Method

RBFNN with Spread= 22.54

% Relative Error

Voltage Magnitude (pu)

Voltage Angle(0)

Voltage Magnitude (pu)

Voltage Angle(0)

Voltage

Angle

20

0.9925

-1.4381

0.9925

-1.4047

0.00

2.32

50 70

0.9875 0.9842

-2.4553 -2.3527

0.9695 0.9683

-2.4564 -2.7866

1.82 1.62

-0.04 -18.44

150

0.9699

-3.8512

0.9558

-4.2166

1.45

-9.49

330 750

0.9336 0.8080

-7.4223 -17.8665

0.9207 0.7970

-7.6626 -17.949

1.38 1.36

-3.24 -0.46

Case 2: Varying the Load at Bus-21 Similar to the case-1, the results are obtained on bus-21 and are shown in Table 2. Table 2: Varying the Load at Bus-21 RPF Method Real Power at Bus-21 (MW) 40 220 310 370 410 450

Voltage Magnitude (pu) 0.9929 0.9145 0.8605 0.8142 0.7750 0.7225

Phase Angle(0) -4.7221 -13.8677 -19.1480 -23.3048 -26.5666 -30.6323

RBFNN With Spread= 23 Voltage Magnitude (pu) 0.9706 0.9138 0.8599 0.8134 0.7757 0.7225

% Relative Error

Phase Angle(0)

Voltage

Angle

-4.6859 -13.7963 -19.1346 -23.3060 -26.4569 -30.6314

2.25 0.08 0.07 0.10 -0.09 0.00

0.77 0.51 0.07 -0.01 0.41 0.00

Figure 4: RPF and RBFN Comparison Curves for Case 1 and 2

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P. Gopi Krishna, T. Gowri Manohar & Faris Al-Namiy

Case 3: Varying the Load at Bus-46 Similar to the case-1, the results are obtained on bus-46 and are shown in Table 3. Table 3: Varying the Load at Bus-46 Real Power at Bus-46 (MW) 20 70 130 250 370 460 590

RPF Method Voltage Phase Magnitude (pu) Angle(0) 0.9984 -4.5106 0.9848 0.9681 0.9283 0.8798 0.8345 0.7398

-6.2212 -8.3348 -12.8141 -17.7866 -22.0467 -29.9703

RBFNN With Spread= 21.22 Voltage Phase Magnitude (pu) Angle(0) 0.9982 -4.5093 0.9848 0.9674 0.9277 0.8793 0.8430 0.7393

% Relative Error Voltage

Angle

0.02

0.03

0.00 0.07 0.06 0.06 -1.02 0.07

0.18 0.20 0.19 0.18 0.17 0.12

-6.2098 -8.3182 -12.7901 -17.7551 -22.0097 -29.9337

Case 4: Varying the Load at Bus-70 Similar to the case-1, the results are obtained on bus-70 and are shown in Table 4. Table 4: Varying the Load at Bus-70 Real Power at Bus-70 (Mw) 170 310 680 910 1370 2020

RPF Method Voltage Phase Magnitude (pu) Angle(0) 0.9939 -4.5554 0.9853 -6.4684 0.9595 -11.7503 0.9410 -15.2532 0.9000 -23.0000 0.8000 -38.0000

RBFNN With Spread= 28 Voltage Phase Magnitude (pu) Angle(0) 0.9939 -4.5674 0.9853 -6.4681 0.9596 -11.7503 0.9416 -15.2533 0.9000 -23.0344 0.7998 -38.0288

% Relative Error Voltage

Angle

0.00 0.00 -0.01 -0.06 0.00 0.03

-0.26 0.00 0.00 0.00 -0.15 -0.08

Figure 5: RPF and RBFN Comparison Curves for Case 3 and 4

On-Line Monitoring of Power System Parameters Using Radial Basis Function Neural Networks

RBFNN TRAINING PERFORMANCE CURVES

Figure 6: RBFNN Training Performance Curve for Bus-17

Figure 7: RBFNN Training Performance Curve for Bus-21

Figure 8: RBFNN Training Performance Curve for Bus-46

269

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P. Gopi Krishna, T. Gowri Manohar & Faris Al-Namiy

Figure 9: RBFNN Training Performance Curve for Bus-70

PROPOSED TEST SYSTEM-2: (PRACTICAL IMPLEMENTATION) Proto type practical construction of all 72-buses in laboratory is quite cumbersome. Therefore an equivalent 2- bus test system shown in Figure 10 is developed in the laboratory and is considered as test system-2. In this test system-2, the two buses are equivalent to any of two buses of test system-1, in that way the considered test system-1 and 2 are relevant and are related to each other. Practical implementation of Figure 10 is shown in Figure 11 and close view of individual circuit of Figure 11 is shown in Figure 12, 13 and 14.

Figure 10: An Equivalent Two-Bus Practical Test System

Figure 11: Practical Implementation of an Equivalent 2-Bus Test System

On-Line Monitoring of Power System Parameters Using Radial Basis Function Neural Networks

271

Figure 12: Close View of Sending End Circuit of Equivalent 2-Bus Test System

Figure 13: Close View of Receiving End Circuit of Equivalent 2-Bus Test System

Figure 14: Close View of Implementation of Neural Network on Hardware for Equivalent 2-Bus Test System

PROPOSED OPERATION STEPS FOR TEST SYSTEM-2

Input is taken from the single phase 230V, 50 HZ supply.

A potential transformer is connected in sending end circuit and in receiving end circuit is used to generate the voltage signal.

The voltage signal (V) is applied to Analog to Digital Converter (ADC) in the Arduino boards (PCB 1&2).

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P. Gopi Krishna, T. Gowri Manohar & Faris Al-Namiy

A current transformer connected in series with phase in sending end circuit and in receiving end circuit is used to generate the current signal.

The current signal (I) is applied to another ADC in the Arduino boards (PCB 1 &2).

Both (V & I) of these digital signal gets multiplied, thus power is calculated at both the sending and receiving ends.

The sending end power which is calculated in the Arduino board (PCB1) is fed to the serial RF transmitter.

The output signal of the serial RF transmitter is received by the serial RF receiver connected in the receiving end Arduino (PCB2).

The receiving end Arduino (PCB2) senses sending and receiving end power and compute power losses by comparing both powers. The losses that are obtained from the receiving end Arduino (PCB2) are displayed on LCD (Liquid Crystal Display).

The PCB2 sends the sending end power as test patterns to the trained RBFNN in MATLAB through the serial port, the corresponding RBFNN results presented in Table 1 to 4 were displayed on the LCD through Arduino (PCB3). In this way a novel online monitoring of power system can be achieved using proposed test system-2. These steps can easy understand using the proposed flow chart shown in Figure 15.

PROPOSED FLOW CHART FOR TEST SYSTEM-2

Figure 15: Flow Chart for Operation of Test System-2

273

On-Line Monitoring of Power System Parameters Using Radial Basis Function Neural Networks

RESULTS FROM TEST SYSTEM-2 The load at the receiving end is changed and the corresponding power loss in the transmission is obtained as shown in the Table 5. Table 5: Test System-2 Results S. No

Power Transmitted at Sending End (KW)

Power Received at Receiving End (KW)

Power Loss (KW)

1

0.5

0.33

0.16

2

0.5

0.25

0.25

3

0.5

0.16

0.33

4

0.5

0.125

0.375

5

0.33

0.16

0.16

6

0.33

0.14

0.17

7

0.33

0.125

0.208

8

0.25

0.07142

0.17857

In addition to the results presented in Table 5, the RBFNN results presented in Table 1 to Table 4 were also obtained through the proposed test system-2, when the send end power adjusted to value of real power (test patters) presented in Table 1 to Table 4.

CONCLUSIONS In this paper, the on-line monitoring of power system parameters is obtained using practically implemented proposed test system. In achieving this, the Radial Basis Function Neural Network (RBFNN) plays a vital role in continuous monitoring of power system, so the accuracy of trained RBFNN is verified with the simulation data obtained on Indian SREHV 72-bus system (test system-1) and by using different test patterns, finally these results are compared with Repeated Power Flow (RPF). From the results, it is observed that the absolute error between results of RBFNN and RPF is almost negligible and RBFNN training performance is up to the expectation levels in reaching the set goal. The RPF is involving iterations towards the convergent point and it can be only useful in steady state conditions, so these drawbacks would be overcome by RBFNN. Hence the RBFNN is said to be best suitable for on-line monitoring of power system parameters. The same simulation results as obtained from the trained RBFNN will also obtained from the proposed test system, when the sending end power of proposed test system is given as an input to the trained RBFNN on SREHV 72bus. Therefore the operation steps, the operation flow chart, the results presented in this paper of the proposed test system ,analysis of these results shows an easy and best way to the power engineer at different load dispatch centers in effective monitoring of their power system continuously under normal and in abnormal conditions.

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Ruisheng Diao, Kai Sun, Vijay Vittal, et-al (2009), Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements, vol. 24, No. 2, IEEE Transactions on Power Systems.

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A. G. Phadke,(1993) , Synchronized phasor measurements in power systems, vol. 6, no. 2, pp. 10â€“15, IEEE Journal on Computer Applications in Power.

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P. Gopi Krishna and Dr. T. Gowri Manohar (2012), Assessment of Secured Voltage Stability Power Margin of Indian Southern Power Grid Using Integrated and Intelligent Methods, Vol. 7, No. 2, Int. J. on Recent Trends in Engineering and Technology.

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P. Gopi Krishna, T. Gowri Manohar, and G. N. Srinivas (2010), Online monitoring of available transfer capability in deregulated power system using Adaptive Neuro Fuzzy Inference System, PEIE 2010, CCIS 102, pp. 76 â€“ 81,. Springer-Verlag Heidelberg. [Awarded Best Paper with Gold Medal]

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K Bhaskar, VVS Sarma, D Thukaram (2006), Decision support requirements for intelligent operation of southern grid in India, IEEE Power Indian Conference, DOI: 10.1109/POWERI.2006.1632582.

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C Vyjayanthi, D Thukaram,(2011), Evaluation and improvement of generators reactive power margins in interconnected power systems,Vol.5, Issue: 4, Generation, Transmission & Distribution, Institution of Engineering and Technology (IET).

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K. Vaisakh, , G.V. Siva Krishan Rao,(2010),Linear programming based determination of optimal bilateral real power contracts in open access, vol. 32, Issue: 10, Int. J. of Electrical Power and Energy Systems, Elsevier.

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Moody, J., & Darken, C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural computation, 1(2), 281-294.

10. Park, J., & Sandberg, I. W. (1991). Universal approximation using radial-basis-function networks. Neural computation, 3(2), 246-257. 11. Devaraj, D., & Preetha Roselyn, J. (2011). On-line voltage stability assessment using radial basis function network model with reduced input features. International Journal of Electrical Power & Energy Systems, 33(9), 1550-1555. 12. Wehenkel, L. (1997). Machine learning approaches to power-system security assessment. IEEE Expert, 12(5), 6072. 13. Chakrabarti, S., & Jeyasurya, B. (2007). An enhanced Radial Basis Function Network for voltage stability monitoring considering multiple contingencies. Electric power systems research, 77(7), 780-787. 14. Judd, M. D., McArthur, S. D. J., McDonald, J. R., & Farish, O. (2002). Intelligent condition monitoring and asset management. Partial discharge monitoring for power transformers. Power Engineering Journal, 16(6), 297-304. 15. www.arduino.cc/en/Guide/Introduction 16. Krishna, P. G., & Manohar, T. G. (2010). A Novel Hybrid Intelligent Technique For the Analysis of Optimal Load Flow in Deregulated Power System. Vol. 6, No. 2, Int. J. on Recent Trends in Engineering and Technology. 17. Krishna, P. G., & Manohar, T. G. (2006). Voltage stability constrained ATC computations in Deregulated Power System using novel technique.Proceedings of ARPN Journal of Engineering and Applied Sciences.

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AUTHORâ€™S DETAILS

Mr. P. GopiKrishna is pursuing Ph.D in the Department of Electrical and Electronics Engineering at Sri Venkateshwara University, Tirupathi, India and he is working as a Lecturer at Ibra College of Technology, Ibra, OMAN. He has 14 years of teaching experience in Electrical and Electronics Engineering and since 9 years he has been involving in research, related to the application of intelligent techniques like ANN, Fuzzy Logic and ANFIS to solve different problems of electrical power systems. His research interests are Voltage Stability, Available Transfer Capability, Neural Networks, Fuzzy Logic Systems, Adaptive Neuro Fuzzy Inference Systems, Cognitive Systems, Data Acquisition and Support Vector Machines.

Dr. T. Gowri Manohar received the B.Tech, M.Tech and PhD Degrees in Electrical and Electronics Engineering from the S.V.University, Tirupati, India. Presently he is working as an Associate Professor in the department of Electrical and Electronics Engineering S.V.University, Tirupati, India. He is having 16 years of teaching experience and he has published more than 65 numbers of various international and national conferences & journals. He is a senior Member of IEEE and also he is a member in Indian Society for Technical Education. His research areas of interests are Modern Restructured Power Systems, Electrical Drives and Power Quality and harmonics â€“issues & challenges.

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Dr. Faris Salman Majeed Al-Naimy received his B.Sc. (1980) in Electronics and the M.E. in Electronic (1984) at the University of Technology, Iraq. In 2003 he was awarded a Doctor's degree in Computer and Electronics at the Indian Institute of Technology, Roorkee, India. In 2002, he became acting head/deputy head of Electronics and Computer Engineering at Caledonian College of Engineering, Oman affiliation with Glasgow Caledonian University, Scotland, UK. He became the director of Training and developing of Oman Society of Contractors at 2008. Recently, he worked as Head of Engineering at Ibra College of Technology till date. He has an outstanding scientific qualification in research commitment teaching. His current research interests include Circuit Design and Analysis, Power Electronic & Instrumentations, Electronics, Analogue & Digital Electronic, Multirate Systems, Peer-to-Peer Communication, Bluetooth Design and Applications.

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Published on Jun 30, 2013

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