40220130405017

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 5, September – October (2013), pp. 165-172
© IAEME: www.iaeme.com/ijeet.asp
Journal Impact Factor (2013): 5.5028 (Calculated by GISI)
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IJEET
©IAEME

IMPLEMENTATION OF VARIOUS OPTIMIZATION TECHNIQUES TO
SYNCHRONOUS GENERATOR- A SURVEY
N.RATHIKA#1, Dr. A.SENTHIL KUMAR*2, A.ANUSUYA#3
1

(Research Scholar, Anna University & Asst. Prof. EEE Dept. SKP Engineering College, Tamilnadu,
India)
2
(Professor/EEE, Velammal Engineering College, Tamilnadu, India)
3
(PG Scholar, ME Power System Engineering, SKP Engineering College, Tamilnadu, India)

ABSTRACT
This paper portrays different optimization technique for analyzing the synchronous generator.
The intention of this paper is to identify the best optimization for the Synchronous Generator to get
the accurate modeling and parameter estimation. The most common goals are minimizing cost,
maximizing throughput, and/or efficiency.
Keywords: Ant Colony Optimization (ACO), Artificial Bee Colony Optimization (ABCO), Finite
Element method (FEM), Genetic Algorithm, Stand Still Frequency Response (SSFR), Particle
Swarm Optimization (PSO).
I. INTRODUCTION
Optimization is a mathematical chastisement that affairs the discovery of minima and
maxima of functions, subject to so-called constraints. Today, optimization embraces a wide variety
of techniques from Operations Research, artificial intelligence and computer science, and is used to
convalesce business processes in practically all industries. Process optimization is the chastisement
of adjusting a process so as to optimize some stipulated set of parameters without violating some
constraint. The most common goals are minimizing cost, maximizing throughput, and/or efficiency.
This is one of the major quantitative tools in industrial decision-making. When optimizing a process,
the goal is to maximize one or more of the process specifications, while keeping all others within
their constraints. Optimization is influencing the esthetics as well as the structure of products, which
can be seen in the design of robotics and aerospace components, as well as more typical examples
such as cars. The use of optimization can help to ripen the design process with the concept of a
product and inspire entirely new products, by showing what is and isn't possible, and the best shapes
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or other features. Using modeling and rendering tools, which come originally from the world of
industrial design, can evolve product design. Simulation and optimization tools are becoming
extremely sophisticated. Some of Altair's partners' products can show such fine detail that they let
design engineers look at crack propagation in composite materials, Dagg said.
In power system operation the optimization is used for solving the problem of uncertainty
analysis in power system, optimal reconfiguration of electrical distribution network, optimal load
scheduling, reactive power optimization, steady state security regions, optimal power flow, unit
commitment, multiarea system economic dispatch, security constrained economic dispatch, classic
economic dispatch, sensitivity calculation, power flow analysis. In the last two decades several
contemporary heuristic tools have been evolved that facilitates solving optimization problems that
were hitherto difficult or intolerable to solve, these tools include traditional and modern optimization
methods. Which have been developed to solve these power system operation problems and are
classified into three groups (1) Conventional optimization methods including (Unconstrained
optimization approaches, nonlinear programming (NLP), Linear programming (LP), Quadratic
programming (QP), Generalized reduced gradient method, Newton method, Network flow
programming (NFP), Mixed-integer programming (MIP), Interior point (IP) methods). (2)
Intelligence search methods such as Neural network (NN), Evolutionary algorithms (EAs), Tabu
search (TS), Particle swarm optimization (PSO) (3) Nonquantity approaches to address uncertainties
in objectives and constraints including Probabilistic optimization, Fuzzy set applications, Analytic
hierarchical process (AHP). Latterly, genetic algorithms (GA) and particle swarm optimization
(PSO) technique have interested substantial attention among various modern heuristic optimization
techniques. Techniques such as PSO and Genetic Algorithms are inspired by nature, and have proved
that they to be effective solutions to optimization problems.
The need of optimization is essential in synchronous generator for accurate modeling and
parameter estimation. Analytical models for synchronous machines are based on Park’s
transformation which involves superposition of effects in the direct and quadrature axes and these
Park's equation were extended by Crary and Concordia which include any symmetrical stationary
network connected to the armature [1]. The optimization criteria for synchronous generator may be
technical, economical or functional. The technical criteria refer to technical operating performance.
The economic criteria refer to the design of a generator such that its cost is lowest, during its whole
running period. The functional criteria refer to generator designs that ensure a safe functioning
during transient processes and short time emergency operations. By finding the optimum values, for
the structural dimensions and optimal parameter values in design of synchronous generator can attain
total minimum cost which dependent on the current layer of the generator [2].
A power outage (also power cut, blackout, or power failure) is a short- or long-term loss of
the electric power to an area. There are many causes of power failures in an electricity network
includes faults at power stations, damage to electric transmission lines, substations or other parts of
the distribution system, a short circuit, or the overloading of electricity mains. Power failures are
particularly critical at sites institutions such as hospitals, sewage treatment plants, mines, and the like
will usually have backup power sources such as standby generators, which will automatically start up
when electrical power is lost. Other critical systems, such as telecommunications, are also required to
have emergency power. Telephone exchange rooms usually have arrays of lead-acid batteries for
backup and also a socket for connecting a generator during prolonged periods of outage.
Power quality determines the fitness of electrical power to consumerdevices. Synchronization
of the voltage frequency and phase allows electrical systems to function in their intended manner
without significant loss of performance or life. The term is used to describe electric power that drives
an electrical load and the load's ability to function properly. Without the proper power, an electrical
device (or load) may malfunction, fail prematurely or not operate at all. There are many ways in
which electric power can be of poor quality and many more causes of such poor quality power.
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II. DIFFERENT OPTIMIZATION ALGORITHMS
A) Ant Colony Optimization
By applying Ant colony optimization (ACO) for parameter estimation of synchronous
generator stretches the virtues of rapidness and global optimization depend on transient behavior of
the machine and a step added to the field system [3]. For penetrating the optimal point of maximum
load ability point at a load bus the Ant Colony Optimization (ACO) technique are used. By using this
technique the optimal points are identified in the off-line mode, it can abet the power system
operators to execute pilot study prior to envisioned load increment in their transmission system. The
ACO has advantage over the evolutionary programming (EP) and automatic voltage stability analysis
(AVSA) in terms of its accuracy and least computation time and this technique able to reduce
computation burden in an optimization process [4]. ACO is used for hawked problem of parameter
identification in partial discharge PD analysis is secondhand for modeling of the phenomenon of
partial discharges in dielectrics [5]. For solving multi-dimensional continuous space optimization
problems an improved ant colony algorithm are used. An improved ant colony algorithm is applied
for aggregation of generator dynamic parameters from aggregation of generator electromagnetic
circuit and excitation system spectacles viable and available results [6]. OPF is a non-linear and a
hefty combinatorial problem, for these ACO has been successfully applied. This technique has been
developed to be expended for maintenance and repairing planning with 48 to 24 hours expectancy.
The main advantage of this method is for analyzing large set of consequences in OPF, ACO exhibits
low execution time [7].
B) ARTIFICIAL BEE COLONY ALGORITHM
Artificial Bee Colony (ABC) is one of the most recent computational intellects to solve the
optimal power flow (OPF) problems and it is effective than other swarm intelligence methods. Bees
Algorithm can converge for superior solution marginally faster than the rest methods [8]. The social
benefit and the distance of maximum loading can be reduced byan interior point method to solve
multi-objective function for optimal power flow [9]. Economic Dispatch (ED) problem with
generator constraints can be solved by the implementation of Bee Colony Optimization (BCO). BCO
algorithm demonstrates superior characters such as high-quality solution, stable convergence
characteristic and good reckoning efficiency. The method of BCO for solving the constrains of ED
problems attain higher quality solution, efficient and faster computational time than the conformist
methods [10]. The cost minimization and congestion management will be maintained by applying
BCO. Significantly system security is preserved such that the current flow in the power system
network and controlling the transmission constraints carries cost minimization. By optimizing the
generated power in the system the fuel cost will be minimized [9]. The dynamic economic dispatch
problem is elucidated by artificial bee colony (ABC) algorithm. Comparing with hybrid harmony
search (HHS) and adaptive particle swarm both the above method attains “less” operating fuel costs
than that of the ABCalgorithm [11]. In the radial distribution systems by placing Artificial Bee
Colony algorithm (ABC) instead of Distributed Generators (DG), which reduces the real power loss,
green house effects, and to improve supply quality and voltage profile and also reduce line loss and
environment impact. It will achieve less CPU time-consumption and high solution quality. This
algorithm shows its ascendancy and probable for solving complicated power system [12]. The ABC
algorithm solves the optimal dynamic dispatch problem in power system. Which cogitated the
generator nonlinear characteristics, such as valve-point effects and the transmission network losses?
Furthermore, the
mechanism and some parameters of this algorithm are analyzed and their
numerical results show that for certain type of fuel cost functions; the algorithm can provide accurate
dispatch solutions within reasonable time [13]. For rescheduling the generators the Artificial Bee
colony Optimization is used which maintain the congestion management based on choosing the
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generator sensitivity factor (GSF). Based on penalty function methodology the constraints are
handled and effects on other critical lines due to rescheduling have been mentioned in the paper [14].
ical
ELEMENT
C) FINITE-ELEMENT METHOD
The finite-element method is accomplished to salient pole synchronous generator to extent
element
the unbalanced magnetic forces due to eccentric motion of the rotor shaft under no
no-load and loaded
condition.The influences of the stator winding parallel paths and the rotor damper winding on
mitigation of unbalanced magnetic pull can be analyzed by FEM [15]. FEM are used for dissecting
the harmonic distortion factor in output voltage of synchronous generatorby calculating the stator
coil flux linkages and contemplation is given to rotor movement through two-dimensional finitetwo dimensional finite
element modeling. For satisfying the requirement for the maximum acceptable waveform distortion
factor, this technique may be useful for prime pole shape design [16].

Fig 1 Model analysis for FEM
By modernization of hysteresis cycle the modeling in electrical machine can be done for iron
loss improvement. An accurate measuring of magnetic flux density waveform is implemented to
electrical machine after the FEM calculation at various points [17]. 2D FEA are used for concurrent
reduction of iron losses and magnetic noise of a wound-field synchronous machine by cogitating
wound field
various stator tooth lengths and rotor pole air gap surface radii and their impact gives adequate
change during design. To compute iron losses as well as details of the weak coupling between the
iron
magnetic and structural finite element models are used whichcompute the magnetic noise In addition
noise.
to that magnetic forces can be calculated, the couple iron loss and acoustic optimization can be
achieved by FEA, the adequate torque decrease are determined for achieve other benefits. Adequate
choices throughout the design, for driving the tool are achieved by FEM method [18]. The static and
dynamic rotor eccentricities of a synchronous generator at no load are studied by using 3 FEM.
generator
3-D
This method reduces simulation time because it does not require enmeshing for fault case
simulations [19]. The brushless self
self-excited single-phase synchronous generator has been studied
phase
by using direct finite element method and their flux distribution and the characteristics of generator
ent
have been clearly shown. The output voltage is kept near constant for a wide range of load without
AVR [20].

168


D) GENETIC ALGORITHM
The synchronous-machine equivalent circuits parameters from standstill frequency response
data can be obtained by using a hybrid genetic algorithm. The global minimum value can be founded
by genetic algorithm within a search interval of the fitness function. Which are used for matching the
equivalent circuit and the measured machine transfer functions. The transfer functions of a turbine
generator can be determined by Finite-element modeling [21]. During a change in reference voltage
level the optimization problem of minimizing the model’s forecast error can be solved by genetic
algorithm. For hysteretic brushless exciter model parameter can be identified by GA method. The
observed and simulated waveforms of the synchronous generatorerror can be minimized based on the
difference between time-domain systems. It not only provides means for characterizing complex
models, but they should inspire the derivation and utilization of more detailed and state-of-the-art
models. This method proposes great flexibility to the predictor [22]. The genetic algorithm can
design the optimal fuzzy logic controller for the generating unit. The generator terminal voltage and
the rotor angle speeddeviation are given to the inputs as the results both the voltage profile and the
dynamic stability of the generating unit are augmented by designing [23]. The multi-parameter
optimization problems are solved by plagiaristic of population diversity based genetic algorithm
(PDGA), which is used to design an optimal fuzzy excitation controller for synchronous generator. It
is self-optimizing method for designing a FLC by the help of GA. This method reduces the number
of optimized controller parameters, and implies that the method keeps great potential for broader
application [24].
E) HOOK-JEEVES OPTIMIZATION & SSFR TEST
The dynamic parameters of the machine are acquired by using Standstill Frequency Response
(SSFR) test.Hook-Jeeves optimization method is mostly used for parameter estimation purpose. The
SSFR test involve some merits, are require very little power and easy implementation, ready
accessibility of powerful computer tools has alleviated the data logging and analysis procedures, and
it can simultaneously provide the equivalent circuits for both direct and quadrate axes. It is a time
saving method, that estimated model can be used for studying stability and low frequency
oscillations ad design and tuning power system stabilizers of the generator [25]. The SSFR test data
are used to schoolwork the effect of noise on the expected parameters. Theestimationalgorithm
should not be affected by the noise and the estimation technique should require a minimum prior
knowledge, these two issues can be effectively dealt by using maximum likelihood (ML) estimation
[26]. Simulation data for generators from SSFR test can be strong-minded by using of maximum
likelihood technique. The method used for data determination is similarly used for simulation models
and used to calculate the stability and dynamic performance of the machine [27]. The Standstill
Frequency Response Test (SSFR) establishes the direct and quadrature axis operational impedances
for salient pole synchronous machine. It is then applied with the rotor at standstill in a given arbitrary
position, thus avoiding the difficulties in rotor mechanical alignment and rendering it suitable for
large salient pole synchronous machines. Applying a variable-frequency, reduced-voltage source and
measuring the magnitude and phase of the resulting current for each frequency at a specified range
can obtain the machine impedance. The main advantage of this technique is that the machine can be
tested with the rotor at standstill, stopped at an arbitrary position [28]. The empathy of synchronous
machine models can be achieved by direct maximum-likelihood (ML) estimation process based on
the standstill frequency response (SSFR) test data. The results show that by encompassing both the
open and short-circuit the SSFR physiognomies of the two generators canbe precisely represented by
the established high order synchronous models up to 1 kHz [29].

169


F) PARTICLE SWARM OPTIMIZATION
PSO is an intellectual computational method based on a stochastic search. For solving the
complicated engineering problems PSO consider to be versatile and efficient tool. Synchronous
machine model output to be expended as the objective function in modified version of PSO, thus
sanctioning a new more efficient method for parameter identification [30]. For identification of nonlinear system Least Mean Square Algorithm (LMS) and GA Algorithm hold some demerits. In LMS
the weights rattle around and do not converge to optimal solution. In GA contributes slower
convergence rate. PSO technique identifies the nonlinear system and overcome the problem presents
in LMS and GA [31].PSO is mainly expended for optimization problem. For transforming the
identification problem into optimization PSO are used to identify the unknown parameter in this
model and these technique is faster than real time simulation [32].
III. CONCLUSION
The surveys of various optimization techniques for synchronous generator are analyzed and
studied. From the above study it is observed that the PSO technique is best and well suited for
parameter optimization. The uniqueness of this technique is that it can extract accurate value. The
main advantage of this technique is the parameter extraction can be reformulated.
IV. REFERENCE
[1]
[2]
[3]
[4]
[5]

[6]
[7]
[8]
[9]

[10]
[11]
[12]

[13]

[14]

Himanshu Vijay and D.K. Chaturvedi, “ANALYSIS OF PARAMETERS ESTIMATION
TECHNIQUES OF SYNCHRONOUS MACHINE” , NSC, December, 2008
ElisabetaSpunei, Ion Piroi, FlorinaPiroi,“Optimizing Structural Dimensions and Costs of a
Synchronous Generator Depending on the Current Blanket”ANALELE UNIVERSITA.
Lixia sun, Ping Qu, Qixinhuang Ping Ju “parameter identification of synchronous generator using Ant
Colony Optimization Algorithm” 2007 IEEE.
Mohd. RozelyKalil, Ismail Musirin, “Ant Colony Optimization for Maximum Load ability Search in
Voltage Control Study”, First International Power and Energy Conference PEC on 2006.
R.Candela and E.Rivasanseverino “Partial Discharge analysis and parameter identification by
continuous Ant Colony Optimization” IEEE 2008.
Yidizhang, Lin Guan “Application of Ant Colony Algorithm in Generator Dynamic Parameter
Aggregation” IEEE 2011.
J. Soares, T. Sousa,1Z. A. Vale, H. Morais, P. Faria “Ant Colony Search Algorithm for the Optimal
Power Flow Problem” IEEE 2011.
C. Sumpavakup, I. Srikun, and S. Chusanapiputt “A Solution to the Optimal Power Flow Using
Artificial Bee Colony Algorithm” International Conference on Power System Technology 2010.
M. A. Rahim, Ismail Musirin, IzhamZainalAbidin, Muhammad Murtadha Othman, “Contingency
Based Congestion Management and Cost Minimization Using Bee Colony Optimization Technique”
International Conference on Power and Energy (PECon2010).
C. Chokpanyasuwant, S. Anantasate, S. Pothiya,We Pattaraprakom, and P. Bhasaputra “Honey Bee
Colony Optimization to solve Economic Dispatch Problem with Generator Constraints” IEEE 2009.
Fahad S. Abu-Mouti, Mohamed E. El-Hawary “Optimal Dynamic Economic Dispatch Including
Renewable Energy Source using Artificial Bee Colony Algorithm” IEEE 2012.
IsrafilHussain, Anjan Kumar Roy “Optimal Distributed Generation Allocation in Distribution
Systems Employing Modified Artificial Bee Colony Algorithm to Reduce Losses and Improve
Voltage Profile” International Conference On Advances In Engineering, Science And Management,
IEEE 2012.
Li Zhang, Gao-xiaWang,JunZhu,Xiao-Gang He,Peng Liu, “Artificial Bee Colony Algorithm for
Optimal Dynamics Dispatch Problem” 4th International Conference on Intelligent Human-Machine
Systems and Cybernetics 2012.
Subhasish Deb and Arup Kumar Goswami, “Congestion Management by Generator Rescheduling
using Artificial Bee Colony Optimization Technique” IEEE 2012.
170

[15] DamirZarko, Member, IEEE, Drago Ban, Ivan Vazdar, and Vladimir Jaric “Calculation of
Unbalanced Magnetic Pull in a Salient-Pole Synchronous Generator Using Finite-Element Method
and Measured Shaft Orbit” IEEE 2011.
[16] Chang Eob Kim, and J. K. Sykulski, “Harmonic Analysis of Output Voltage in Synchronous
Generator Using Finite-Element Method Taking Account of the Movement” EEE TRANSACTIONS
ON MAGNETICS, VOL. 38, NO. 2, MARCH 2002.
[17] AnthonyFrias, Afefkedous-lebouc,ChristianChillet, Laurend Albert, Lionel Calegrari “ Improved and
validation of an Irin loss model for synchronous machine” IEEE 2012.
[18] Anthony Frias, Pierre Pellere, AfefKedousLebouc, Christian Chillee, Vincent Lanfranchi, Guy
Friedrich, Laurent Alberti, Louis Humbert, “Rotor and Stator Shape Optimization of a Synchronous
Machine to Reduce Iron Losses and Acoustic Noise” Vehicle Power and Propulsion Conference,
IEEE 2012.
[19] B. A. T. Iamamura, Y. Le Menach, A. Tounzi, N. Sadowski, and E. Guillot “Study of Static and
Dynamic Eccentricities of a Synchronous Generator Using 3-D FEM” IEEE 2010.
[20] Sakataronanaka and katsumikesamaru “Analysis of New Brushless Self-Excited Single Phase
Synchronous Generator by Finite Element Method” IEEE.
[21] RafaelEscarela-Perez, Member, IEEE, TadeuszNiewierowicz, and Eduardo Campero-Littlewood,
“Synchronous Machine Parameters from Frequency-Response Finite-Element Simulations and
Genetic Algorithms” EEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 16, NO. 2, JUNE
2001.
[22] Dionysios C. Aliprantis, Scott D. Sudhoff, Brian T. Kuhn, “Genetic Algorithm-Based Parameter
Identification of a Hysteretic Brushless Exciter Model” IEEE TRANSACTIONS ON ENERGY
CONVERSION, VOL. 21, NO. 1, MARCH 2006.
[23] J i y u Wen Shijie Cheng, O.P. Malik, “A SYNCHRONOUS GENERATOR FUZZY EXCITATION
CONTROLLER 0 LY DESIGNED WITH A GEiWTICALGORITHM” IEEE 1997.
[24] J. Y. Wen Q. H. Wu D. W. Shimmin" D. R. Turner S. J. Cheng “Population Diversity Based Genetic
Algorithm For Fuzzy Control of Synchronous Generators” IEEE 1999.
[25] M.R. Aghamohammadi , M. Pourgholi, “Experience with SSFR Test for Synchronous Generator
Model Identification Using Hook-Jeeves Optimization Method” INTERNATIONAL JOURNAL OF
SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENT Issue 3, Volume 2, 2008.
[26] A. Keyhani, S. Hao, G. Dayal, “Maximum Likelihood Estimation of Solid-Rotor Synchronous
Machine Parameters from SSFR Test Data” 89WM224-7 September 1989.
[27] Ali Keyhani and shangyouHao “Maximum likelihood estimation of generator stability constants using
SSFR test data” 1991.
[28] Edson da Costa Bortoniand José AntônioJardini “A Standstill Frequency Response Method for Large
Salient Pole Synchronous Machines” IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL.
19, NO. 4, DECEMBER 2004.
[29] A. Keyhani, H. Tsai, “Identification of High-Order Synchronous Generator Models from SSFR Test
Data” IEEE Transactions on Energy Conversion, Vol. 9, No. 3, September 1994.
[30] Graeme Hutchison, Bashar Zahawi, Damian Giaouris, Keith Harmer, Bruce Stedall “Parameter
Estimation of Synchronous Machines Using Particle Swarm Optimization”IEEE 2010.
[31] G.Panda, D.Mohanty,BabitaMajhi and G.Sahoo, “ Identification of Non linear system using Particle
Swarm Optimization Technique” IEEE 2007.
[32] Li Liu,Wenxin Liu, David A.Cartes, Nianzhang “ Real time Implementation of particle swarm
optimization based model parameter identification and an application” IEEE 2008.
[33] G.Vasu, J. Nancy Namratha and V.Rambabu, “Large Scale Linear Dynamic System Reduction using
Artificial Bee Colony Optimization Algorithm”, International Journal of Electrical Engineering &
Technology (IJEET), Volume 3, Issue 1, 2012, pp. 145 - 155, ISSN Print : 0976-6545, ISSN Online:
0976-6553.
[34] Sumit Kumar and Prof. Dr. A.A Godbole, “Performance Improvement of Synchronous Generator by
Stator Winding Design”, International Journal of Electrical Engineering & Technology (IJEET),
Volume 4, Issue 3, 2013, pp. 29 - 34, ISSN Print : 0976-6545, ISSN Online: 0976-6553.
 

 

 

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BIBLIOGRAPHY

Dr. A.Senthil Kumar, obtained is Bachelor’s Degree (1996) in
Electrical and Electronics Engineering in first class from University of
Madras, Chennai, Tamil Nadu. He obtained is Master’s degree (2000) in
Power Electronics and Drives in first class from Bharathidasan University,
Trichy, Tamil nadu and also he obtained is Master’s degree (2006) in
Human Resource Management in first class from TNOU, Chennai. He
completed his Doctoral degree (2010) in the area of Electrical Engineering
from Indian Institute of Technology Roorkee, Uttarakhad, India. He also
completed his Post Doctoral research fellow in Centre for Energy and Electrical Power, Electrical
Engineering Department, Faculty of Engineering and built environment, Tshwane University of
Technology, Pretoria, South Africa for a period of one year from 2012-2013. He obtained many
awards and certificates during M.E and Ph.D studies. He has 17 years of teaching and research
experience. He has published 1 5 papers in international journals and presented 30 papers in
international and national conferences. He has attended many international seminars and
workshops. He is a life member of many professional body memberships like ISTE, IEI, CSI,
IAENG, IACSIT etc., He visited foreign country such as Hong Kong, Chengudu & Mauritius
financially supported by DST, CSIR and NRF. He has delivered state of the art lectures in many
educational institutions and professional societies. He is currently doing ongoing project funded by
AICTE worth of 33 lakhs. His research interests include Multiphase Machines, Power Electronics,
Renewable Energy Generation Source, Microcontroller & VLSI application in Power Electronics &
Electric Drives, Active Filters Stability and System Analysis. Currently he is working as
Professor/EEE at Velammal Engineering College, Chennai, Tamil Nadu.
N.Rathika, obtained her BE (2000) Electrical and Electronics
Engineering degree with first class from University of Madras and
completed her M.E(2006) in Power Electronics and Industrial Drives in
first class with distinction from Sathyabama University. She has 12 years of
teaching experience. She is a PhD research scholar of Anna University,
Chennai. She has attended many Faculty Development Programmes and
Workshops. She presented 6 papers in International & national
Conferences. At present she is working as Assistant Professor in EEE
department of SKP Engineering College, Tiruvannamalai, Tamilnadu,
India. She is a member of IEEE, ISTE. Her areas of interest are Electrical Machines, Power
Electronics, Electrical Drives,and Soft computing Techniques.

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