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Abstract Too much dependence on large, polluting andexpensive generation is no longer an option that Canadians

would endorse in this era of distributed generation through

renewable energy systems. Understanding the significance and

prospects of self-excited induction generators (SEIGs) in

distributed wind power generation, this paper presents an

exclusive study of fault and a artificial neural network (ANN)

based technique for its detection across the stator terminals of the

SEIG. Firstly, two-axis model of a 7.5 hp industrial copper-rotor

SEIG is developed to perform numerical investigations under

static loading conditions, faulty conditions and hence derive data

for designing the ANN based detection scheme. Fault tolerant

capability of the machine is experimentally elicited by applying a

short-circuit fault across the terminals of the machine and the

need for fault detection in the SEIG system is discussed. Lastly, a

novel ANN based scheme is developed for fault detection and

numerical investigations are performed to illustrate the

performance of the developed scheme. This paper aims to

provide a good study to understand and develop a ANN based

device for fault detection in a SEIG system.

Index TermsArtificial neural network, copper-rotor

induction machine, fault detection, self-excited induction

generator.

I. INTRODUCTION

Distributed energy technologies are playing anincreasingly important role in the nation's energy portfolio.They can be used to meet peaking power, backup power,remote power, power quality, as well as cooling and heatingneeds. Distributed generation also has the potential to mitigatecongestion in transmission lines, reduce the impact ofelectricity price fluctuations, and strengthen energy security.Distributed wind power generators are small compared totypical central-station power plants and provide uniquebenefits that are not available from centralized electricitygeneration. Many of these benefits stem from the fact that the

generating units are inherently modular, which makesdistributed power highly flexible [1].Reference [2] illustrates some examples of distributed

wind power generation plants in Canada. The Yukon EnergyCorporation of Canada installed a 150 kW wind energygeneration system on Haeckel Hill, a shoulder of Mt.Sumanik, at an altitude of 1,430 m, approximately 750 mabove the valley floor where the territorys capital,Whitehorse, is located. The Whitehorse grid, which is isolatedfrom Canada's national electrical grid, also hosts 0.8 MWwind turbine capacity, provided at Haeckel Hill. A smallstand-alone system installed in Southern Alberta allows a farm

to operate independently of the grid. The farm had beenconnected to the grid, but the owner wished to have the farmautonomously powered, to reduce the environmental impact ofhis farm and home energy use. The farms wind energy systemsupplies power to a residence for a family of four, a machineshop, a water well and yard lights. The rolling prairies ofAlberta, between Calgary and Red Deer, are one of the mostproductive agricultural areas in Western Canada. A wheatfarmer, who wanted independence from the electric utility,

purchased a 10 kW wind turbine to supply all of his powerrequirements. The Trochu Wheat Farm was already connectedto a power grid, but the farmers goal was a stand -alonesystem that would survive inflation and have lessenvironmental impact in comparison to the coal used toproduce electricity for the grid.

Many developing countries have renewable energyresources in abundance, but these resources are mostly locatedin the remote regions, thereby, creating a number of issues fortheir deployment. The induction machine has gainedconsiderable attention as a wind power generator in twomodes of operations, namely, in the self-excited mode and inthe grid-connected mode. A self-excited induction generator

(SEIG) is an ideally suited electricity generating system fordistributed wind energy as it becomes tedious and highlyexpensive to lay transmission lines over or under water,through mountainous areas and across long distances. A stand-alone SEIG driven by wind turbine is capable of supplyingpower to domestic, industrial and agricultural loads,particularly in the remote and hilly areas where theconventional grid supply is not available. Installation of theSEIG reduces the high maintenance and installation costs aslarge amounts of metal and raw material use can beminimized, infrastructure and transmission losses which occurwhen a regular power grid or transmission lines are installed.Over the years SEIG has emerged as an alternative to the

conventional synchronous generator for such applications [3].Commercially available induction motors can be used asSEIGs for small scale wind farms. The nameplate efficiency ofa practical, in-service, 15 hp, 1,800 rpm conventionalaluminum-rotor induction machine today is about 89.5%,which is below the 1997 Energy Policy Act standard of 91%.As demonstrated by many other researchers, the adoption ofcopper rotors should bring efficiencies to the 94 to 96% rangeexceeding the requirements of todays NEMA premiumefficiency motor, nominally 93% [4]. In addition, analyses bymotor manufacturers have shown that copper rotors can beemployed to reduce overall manufacturing costs at a given

Fault Detection in Copper-rotor SEIG

System Using Artificial Neural Network for

Distributed Wind Power Generation1K. Lakshmi Varaha Iyer, Student Member, IEEE, 2Xiaomin Lu, Student Member, IEEE,

3

Kaushik Mukherjee, Member, IEEE,

and4

Narayan C. Kar, Senior Member, IEEECentre for Hybrid Automotive Research and Green Energy, University of Windsor, ON, Canada N9B 3P4

1iyerl@uwindsor.ca, 2lu117@uwindsor.ca, 3kmukh@uwindsor.ca, and 4nkar@uwindsor.ca

978-1-4673-0142-8/12/$26.00 2012 IEEE 1700

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efficiency or to reduce motor weight, depending on aparticular attribute the designer chooses to emphasize. Theenergy savings achievable through the use of copper rotors issubstantial. The U.S. Department of Energy reports thatmotors above 1/6 hp use about 60% of all electricity generatedin the United States and the medium power motors (1 to 25hp) are the favoured candidates for conversion to copper rotors[5]. In Canada alone, 1% increase in the motor electricalenergy efficiency would save roughly $200 million and as aresult, 0.5 million barrels of oil annually. As Canada and theworld move rapidly towards increased dependence on windpower generation, copper bars can play an important role inthe rotor construction of SEIG [6]. Hence, the niche copper-rotor induction machine is considered here for investigations.

Major problems in a SEIG system such as voltage

regulation, frequency regulation, fault detection and islanding

still attract continued research and development. Faults across

the high-voltage terminals of the generator lead to economic

losses and power outages. The SEIG is attractive for

distributed wind power generation as the terminal voltages of

the system collapse during short-circuit faults and hence, the

excitation of the machine is cut-off driving the machine to just

run freely at the wind turbine rotor speed. This makes SEIG

more attractive for such power generation as the machine is

fault tolerant and the general consumer does not have to worry

about replacing or repairing the machine. However, it is

necessary for the fault to be detected and communicated to the

operator in order to resume operation after fault inspection and

clearance. Fault here cannot be detected using conventional

schemes using over-current sensors followed by protection as

the voltage and current collapse within a few cycles during a

fault. Fast and accurate fault detection will not only render

immediate corrective action but also protect sensitive

equipment along the line and prevent the domino effect.

Thus, it is of vital importance to rapidly detect and identifyfaults, assist the task of repair and maintenance, and reduce the

economic effects of power interruption.

Thus establishing the significance of fault detection in

distributed wind power generation using SEIG, this paper

proposes an exclusive artificial neural network based fault

detection scheme for SEIG system. Section II of this paper

TABLE 1INDUCTION GENERATORDATA

Parameters Copper-rotor SEIG

Output power 7.5 hp

Rated voltage 460 V

Rated current 9.5 AConnections Wye

Number of poles 4

Rated speed 1775 rpm

Rated frequency 60 Hz

Rs [] 0.65417

Rr[] 1.48166

ls [] 2.08272

lr[] 3.12267

m[] 68.9616

presents the developed two-axis model of a 7.5 hp copper-

rotor SEIG used in the investigations and also presents an

experimental study to elicit the need for the developed fault

detection scheme. Section III presents the construction and

derivation of the proposed ANN based fault detection scheme.

Also, numerical investigations performed to elicit the

performance of the developed scheme are presented. Hence,

the calculated results are discussed.

II. MATHEMATICAL MODELING AND ANALYSIS OFINDUSTRIAL 7.5 HP COPPER-ROTORSEIG UNDERSTATIC

LOADING AND FAULT INITIATION

A. Mathematical Modeling of the Copper-rotor SEIG

The two-axis model of the copper-rotor SEIG underconsideration is developed using conventional machine

equations based on the dq stator reference frame theory (=0)in order to bring out the performance of the SEIG undervarious loading conditions, faults, and hence use the empiricaldata for designing the ANN based fault detection scheme. Thedq axis stator and rotor voltage-current equations at no-loadconditions can be expressed as shown in (1) and the excitationcapacitor bank can be written as in (2).

dr

qr

ds

qs

rrrrmmr

rrrrmrm

mmsss

mmsss

ds

qs

i

i

i

i

pLRL)(pLL)(

L)(pLRL)(pL

pLLpLRL

LpLLpLR

v

v

0

0

(1)

where, Rs and Ls are the stator resistance and inductance, Rr

and Lr are the rotor resistance and inductance, Lm is the

magnetizing inductance, vqs, vdsand iqs, ids are the q- and d-axis

components of the stator voltage and current, and iqr, idrare the

q- and d-axis components of the rotor current, p is the

differential operator, icq and icd are the capacitor currents along

the direct and quadrature axes, ris the electrical rotor speed,

is the speed of the reference frame and C is the value of

capacitance.

ds

qs

cd

cq

v

v

p

pC

i

i(2)

ld

lql

ds

qsl

ld

lq

i

iRL

v

vL

i

ip

11(3)

The voltage and current equations of the machine underRandRL loads incorporate (3), where, ilq, ildare theload currents

in q and daxis representations,R andLl are the resistance andinductance of the load. Saturation characteristics of themachine were measured at its rated frequency andincorporated in the above modelling by fitting it with anarctangent continuous function as given in [7]. The machineequivalent circuit parameters, resistances and inductancesdetermined from the standard no-load, dc and blocked rotortests, are presented in Table 1.

The calculated results of terminal voltage, stator current andreactive power profiles of the copper-rotor SEIG obtainedthrough a developed computer program using the developedmathematical model are as shown in Figs. 1(a)-(c). The results

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shown in Figs. 1(a) - (c) were elicited under a static RL loadapplied across the stator terminals at around 20 seconds afterthe machine voltage had reached steady state.

The value of the magnetizing reactance plays an importantrole for safe operation of the SEIG. Higher loading conditionpushes the magnetizing reactance to the unsaturated regionand hence the voltage drop occurs as seen in Fig. 1(a). This isthe reason why the induction machine has the ability to protectitself from overloading current.

(a)

(b)

(c)

Fig. 1. Calculated results of copper-rotor SEIG underRL load of 340 and 0.44 H after the machine reached a rated speed of 1 pu at an excitationcapacitance of 39.6 F. (a) Calculated phase voltage profile. (b) Calculatedstator current profile. (c) Calculated reactive power delivered.

Fig. 2. Experimental setup of the DC motor coupled SEIG system used in theinvestigations.

(a)

(b)Fig. 3. Measured short-circuit voltage and current profiles of copper-rotorSEIG system after fault initiation at the stator terminals. (a) Measured statorvoltage. (b) Measured stator current.

B. Experimental Investigation of Fault across the Stator

Terminals of the Copper-rotor SEIG

In order to study the behaviour of the machine under faultyconduction a 3-phase short circuit fault was applied across thestator terminal of the copper-rotor SEIG. The experimentalsetup of the DC motor coupled SEIG system is as shown inFig. 2. The transient behaviour of the machine was observedon a Tektronix-2024 oscilloscope which has a sampling rate of

2 Giga samples. Figs. 3(a) and (b) show the stator voltage andcurrent waveforms captured using the oscilloscope duringfault initiation. The negative x-axis shows the pre-faultconditions.

Analysing the above figures, the most observablephenomenon is the fast decay of voltage across the statorterminals on application of the 3-phase fault. Though shortcircuiting of an SEIG appears to instantaneously de-excite thestator winding, thus causing voltage collapse across the statorterminals, there is a transient state preceding the completedecay of voltage. Owing to the inductive nature of themachine, the machine opposes the sudden change in current inthe circuit, but due to instant collapse of voltage, the current

decays almost completely in 0.07 seconds after the faultinitiation. However, the current does not completely decayuntil 0.1s, hence, if the fault is cleared within 0.1s after itsinitiation, the pre-fault conditions can be regained instantly. Incase, the fault is not cleared within the stipulated time, theSEIG has to re-excite as it has lost its residual magnetism. There-excitation time does not equal the previous no-loadexcitation time of the SEIG because of the effects of fault onthe residual magnetism of the SEIG which play a pivotal rolein the excitation time [8], [9]. The nature of the transientsdepends on factors such as saturation level, excitation

-400

-300

-200

-100

0

100

200

300

400

-0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04

Phase Voltage

Time [Sec]

PhaseVoltage[V]

-80

-60

-40

-20

0

20

40

-0.03 -0.01 0.01 0.03 0.05 0.07

Stator Current

Time [Sec]

Current[A]

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capacitances, discharge times, rotor damping, instant of faultinitiation, etc., in the system.

Thus, the above study clearly explains that the SEIG isattractive for distributed wind power generation as theterminal voltages of the system collapse during short-circuitfaults and hence, the excitation of the machine is cut-offdriving the machine to just run freely at the wind turbine rotorspeed. Fault here cannot be detected using conventionalschemes using current sensors as the voltage and currentcollapse within a few cycles during a fault. Hence, a novelANN based fault detection scheme is proposed in this researchmanuscript.

III. MODELING AND ANALYSIS OF ARTIFICIALNEURALNETWORKBASED SCHEME FORFAULT DETECTION

A. Construction of the Chebychev Polynomial based Artificial

Neural Network

The ANN employed in this paper is constructed of threelayers, input-, hidden- and output-layer, as shown in Fig. 4,[10]-[12]. The input-layer of the constructed neural networkemploys a linear function x(i)=t, (i=1, 2, n, n number of

samples)as its activation function, and the hidden neurons areactivated with Chebyshev polynomials j (j=0,1, 2, m)

where m denotes the number of hidden neurons and mj 0 isdefined as follows:

)()()(2)(

)()(

1)(

)(

11

1

0

iiixi

ixi

i

i

jjj

(4)

The input-output relation in the artificial neural networkcould be given as follows:

)()(0

iwiy jm

jj

(5)

The parameters miwi ...,2,1, denote the weights betweenthe hidden-layer and output-layer neurons. Assuming n pairsof training data sets {(X, Y)}, X, YR1n matrix, obtainedfrom monitoring the stator current of the machine, theperformance function can be defined as follows:

21

2

1)( iyiywe

n

i

(6)

Fig. 4. Architecture of the artificial neural network.

Based on negative gradient descent method, the weights-iterative formula for the proposed neural network could bedefined as:

)(

)()()1( kww

j

j

jj jjw

wekwkw

(7)

where, k =1, 2,, itermax, denotes the kth iterative, is thelearning rate. Following the equation (4), we can obtain,

)(

)(

...

)2(

)1(

...

)(...)()(

............

)2(...)2()2(

)1(...)1()1(

)(...)2()1(

............

)(...)2()1(

)(...)2()1(

/)))()((21()(

1

0

1

10

10

111

000

1

2

0

YW

ny

y

y

w

w

w

nnn

n

n

n

wiyiwWWe

T

mmn

m

m

mmm

n

i

m

jjj

(8)

where, weights vector W, its corresponding hidden-layerneurons , and input vector Y of the neural network aredefined respectively as follows:

mwwwW Tm 1]...[ 10

mn

mn

m

m

R

nnn

)(...)()(

............

)2(...)2()2(

)1(...)1()1(

1

10

10

nnyyyY T 1)](...)2()1([

By substituting equation (8) into equation (7), we couldhave the weights-iterative formula as

))(()()1( YkWkWkW T (9)Now, let >0 small enough to guarantee the convergence of

training procedure, we can solve the optimal weights of theneural network by using iterative equation (9).

We have derived the weights-iterative formula for theartificial neural network. In fact, for this special structureneural network model, it can globally converge to its optimal

weights if the learning rate is small enough; i.e., thefollowing theorem [13].

Theorem: When)(

20

max

*

T , the iterative

weights series 0)( kkW in (11) will converge to optimal

weights vector YW* , where )(max

T denotes the

maximum eigenvalue of )( T

,and denotes the pseudo-inverse of matrix .Proof: Following eq. (6) through (11), we have:

Hidden Layer

x[1]

x [2]

x[i]

x[1]

x[n]

x[1]j

x[1]0

x[1]1

x[1]m

W

X, Y

+

ANN Learning rule

Input Layer Output Layer

w0

w1

wj

w2N

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2/22

))(()2)(max

())((

22

))((2/22

))(()(max

2))((

22

))((2/22

))((2))((

))((())(((2/22

))((2))((

2/22))(()(

2/22

)1(

2/))1(())1((

2

1

)()1())(

(2

1

))1((

kWeT

kWe

kWekWeTkWe

kWekWekWe

YkWTTkWekWekWe

YkWEkW

YkW

YkWT

YkW

n

i

iykWTj

kWe

Under condition that)(

20

max

*

T, it is known

02/

2

2))(()2)(( max kWET

.Thus, ))(())1(( kWEkWE

In addition, it can be inferred from the definition ofe(w) in (6)

that when)(

20

max

*

T, e(W(k)) is a non-

negative bounded and descending sequence, therefore, must

converge to a certain point. Furthermore, ,0))((lim

kWEk

that is, ,0))((lim

YkWT

kYkW

k

)(lim .

Thus the convergence is proved.

B. Numerical Investigation and Analysis of the Developed

Artificial Neural Network Based Fault Detection Scheme

In order to evaluate the performance of the developed ANNbased detection scheme numerical investigations wereperformed using a developed computer program mainly basedon equations (4), (5), (6) and (9). The developed model of theSEIG presented in section 2 was tested repeatedly for 3-phaseshort-circuit faults across the stator terminals of the machineand the waveform of the stator current obtained was fed intothe developed ANN based detection block. Firstly, thedeveloped ANN based scheme was tested for convergencewith the stator current waveform as the ANN has to track the

stator current waveform during healthy and unhealthyconditions of the system in order to accurately detect anyabnormality in the stator current through pattern classification.A sampling rate of 2 kHz was chosen for this work. n waschosen to be 20 and m as 16.

Fig. 5 shows the stator current waveform obtained as anoutput of the developed copper-rotor SEIG model in case ofhealthy operation of the entire system and the output of theANN based detection block which is a trace of the input statorcurrent. It can be seen from the figure that the developed ANNscheme can track the stator current with very high rate ofaccuracy. Fig. 6 shows the tracking ability of the ANN based

scheme to track the stator current even during an event of 3-phase short-circuit fault across the stator terminals of thecopper-rotor SEIG.

Thus, it is proved that the ANN based scheme canaccurately track the stator current waveform at normal and

Fig. 5. Stator current tracking capability of the ANN based scheme duringhealthy operation of the copper-rotor SEIG system.

Fig. 6. Stator current tracking capability of the ANN based scheme during 3-phase short-circuit fault in the copper-rotor SEIG system.

Fig. 7. Pattern and energy projected on the basis for healthy operation of thecopper-rotor SEIG system .

Fig. 8. Pattern and energy projected on the basis during 3 phase short-circuitfault in the copper-rotor SEIG system.

-7

-5

-3

-1

1

3

5

7

0 0.01 0.02 0.03 0.04

Current[A]

Time [Sec]

Stator current ANN Output

-50

-40

-30

-20

-10

0

10

2030

40

50

0 0.05 0.1 0.15 0.2

Current[A]

Time [Sec]

Stator current ANN Output

0

400

800

1200

1600

2000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Basis

Energy

0

400

800

1200

1600

2000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Basis

Energy

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faulty conditions of the system. This feature is of primaryimportance for fault detection here.

The unique fault signature during the short-circuit fault canbe obtained by analyzing the weights wj corresponding to eachbase j. During normal or healthy condition of thesystem theweights and hence the energyprojected on the basis wouldfollow a unique pattern which is completely different from thepattern exhibited during the fault. Moreover, the failuresignature can also be detected by monitoring the amplitudes ofthe energy corresponding to each base.

Figs. 7 and 8 show the energy projected on the basis forhealthy and faulty condition respectively. It can be seen fromthe figures that the failure signatures for the healthy and faultyconditions are unique in each of the cases, which make it easyto detect the 3 phase short circuit fault accurately within acycle.

IV. CONCLUSION

This paper firstly discusses develops a two-axis model ofniche copper-rotor SEIG to perform numerical investigationsand understand the system under static loading and faultconditions. Fault tolerant capability of the machine isexperimentally elicited by applying a short-circuit fault acrossthe terminals of the machine and the need for fault detection inthe SEIG system is discussed. The empirical data derived fromthe numerical and experimental investigations is hence used todesign an exclusive ANN based fault detection scheme forSEIGs. The developed ANN based detection scheme is thentested and the findings are analyzed. The ANN based schemeis found to accurately detect the 3 phase short-circuit fault.Future work would be towards testing the scheme for all typesof faults such as line-ground, line-line etc. before developing aANN based device for fault detection in SEIGs.

V. REFERENCES

[1] National Laboratory of the U.S. Department of Energy. DistributedEnergy Basis [Online]. Available: http://www.nrel.gov/.

[2] Canada Wind Energy Association. (2010). Small wind: case studies andsuccess stories [Online]. Available: http://www.canwea.ca/swe/.

[3] B. Singh, M. Singh, and A. K. Tandon, Transient performance ofseries-compensated three phase self excited induction generator feedingdynamic loads,IEEE Trans. Ind. Appl., vol. 46, pp. 1271-1280, 2010.

[4] J. G. Cowie, D. T. Peters, and D. T. Brender, Die-cast copper rotorsfor improved motor performance, in Proc.IEEE Pulp and Paper

Industry Technical Conference, 2003.[5] Canadian Copper and Brass Development Association (2006, April).

Wind power and copper in Canada [Online]. Available:http://coppercanada.ca/ .

[6] Canadian Copper and Brass Development Association (2006, April).Technology transfer report - The die cast copper rotor motor [Online].Available: http://coppercanada.ca/.

[7] S. C. Kuo, and L. Wang, Steady-state performance and dynamicstability of a self excited induction generator feeding an inductionmotor, inProc. 2000 IEEE Inte. Conf. on Electric Machines, pp. 1143-1147.

[8] S. K. Jain, J. D. Sharma, and S. P Singh, Transient performance ofthree-phase self-excited induction generator during balanced andunbalanced faults,IEEE Proceedings of Gen., Trans. and Distri.,2002.

[9] R. Wamkeue and I. Kamwa, Numerical modeling and simulation ofsaturated unbalanced electromechanical transients of self-excitedinduction generators, in Proc. of IEEE Canadian conference on

Electrical and Computer Engineering, vol.2, pp. 1147-1151, 2000.[10] J. Upendar, C. P. Gupta, G. K. Singh, and G. Ramakrishna, PSO and

ANN-based fault classification for protective relaying, IET Gen.,Trans., & Dist., 2009.

[11] G. K. Purushotama, A. U. Narendranath, D. Thukaram, and K.Parhasarathy, ANN applications in fault locators, Elec. Power Energy

Sys., pp. 491-506, 2001.[12] Y. Zhang, W. Li, C. Yi, and K. Chen, A weights -directly-determined

simple neural network for non-linear system identification, in Proc.IEEE International Conference on Fuzzy Systems, 2008, pp. 455-460

[13] Y. Zhang and J. Wang, Global Exponential Stability of RecurrentNeural Networks for Synthesizing Linear Feedback Control Systemsvia Pole Assignment, IEEE Transactions on Neural Networks, 2002,13(3): 633-644.

VI. BIOGRAPHIES

K. Lakshmi Varaha Iyerreceivedthe B.Tech. degree inElectronics and Communication Engineering fromSASTRA University, India, in the year 2009 and theM.A.Sc. degree in Electrical and Computer Engineeringfrom University of Windsor, Canada in the year 2011. Heis currently a Research Associate at the Centre for HybridAutomotive Research and Green Energy, University ofWindsor, Canada. His research presently focuses on

design & control of electric machines and conditionmonitoring for renewable energy applications.

Xiaomin Lu received her Bachelor in Engineering fromSun-Yet Sen University, China in July, 2010. She iscurrently working towards her M.A.Sc degree atUniversity of Windsor, Ontario, Canada. Her researchareas include modeling and analysis of permanent magnetsynchronous machines & drives and condition monitoringfor electric vehicle drive-train system and power systemapplications.

Kaushik Mukherjee (M03) was born in 1970. Hereceived the B.E. degree from the Department ofElectrical Engineering, Jadavpur University, Calcutta,India, in 1993, the M.E. degree from the Department ofElectrical Engineering, Bengal Engineering College,

Howrah, India, in 1998, and the Ph.D. degree from theDepartment of Electrical Engineering, Indian Institute ofTechnology, Kharagpur, India, in 2003. Since 1993, hehas spent almost two and a half years in the industry. In

2002, he joined the Department of Electrical Engineering, JadavpurUniversity, India as a Lecturer. From 2006 onwards, he is an AssistantProfessor in the Department of Electrical Engineering, Bengal Engineeringand Science University, Howrah, India. Dr. Mukherjee is presently a VisitingProfessor at the Centre for Hybrid Automotive Research & Green Energy,University of Windsor, Canada. His research interests include electricalmachine drives and power electronics applications in general.

Narayan C. Kar received the B.Sc. degree in ElectricalEngineering from Bangladesh University of Engineeringand Technology, Dhaka, Bangladesh, in 1992 and theM.Sc. and Ph.D. degrees in electrical engineering fromKitami Institute of Technology, Hokkaido, Japan, in 1997

and 2000, respectively. He is an associate professor in theElectrical and Computer Engineering Department at theUniversity of Windsor, Canada where he holds the

Canada Research Chair position in hybrid drivetrain systems. His researchpresently focuses on the analysis, design and control of permanent magnetsynchronous, induction and switched reluctance machines for hybrid electricvehicle and wind power applications, testing and performance analysis of

batteries and development of optimization techniques for hybrid energymanagement system. He is a Senior Member of the IEEE.

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