International Journal of Computer Applications (0975 8887)Volume 3 No.1, June 2010 6 Efficiency Optimization of Induction Motor Drive using Soft Computing Techniques K.Ranjith Kumar D.Sakthibala, Dr.S.Palaniswami Senior Lecturer PG Scholar Professor Dept. of Electrical EngineeringPower Systems Engineering Dept. of Electrical Engg., Govt College of Technology, Govt College of Technology, Govt College of Technology Coimbatore, Tamil Nadu, India.Coimbatore, Tamil Nadu, India.Coimbatore, Tamil Nadu, India.

ABSTRACT This paper presents a new approach that minimizes copper & iron lossesandoptimizestheefficiencyofavariablespeedInduction motordrive.Thismethodisbasedonasimpleinductionmotor fieldorientedcontrolmodelincludesironlossesusesonly conventionalIMparameters.Inliterature,Fuzzylogicand Genetic Algorithms have been used for efficiency optimization of inductionmotordrives.ThispaperproposesintegrationofFuzzy model identification and PSO algorithm for loss minimization. An improvementofefficiencyisobtainedbyadjustingthe magnetizing current component with respect to the torque current component to give the minimum total copper and iron losses. The wholecircuitissimulatedusingMATLAB7.6.Theproposed methodiscomparedwithothersoftcomputingtechniques.The results obtained by Fuzzy PSO shows better results compared with other approaches. General Terms Algorithms, Performance, Verification Keywords: Efficiency,Optimization,Field-OrientedControl(FOC), InductionMotor(IM),FuzzyControllerDesign,Genetic Algorithm (GA), Particle Swarm Optimization(PSO). 1. INTRODUCTION Theutilityofinductionmotorsaremorethan50%ofthetotal electricenergygeneratedworldwide.Asmallimprovementin efficiencywouldsignificantlysavethetotalelectricenergy. Hence,itisimportanttooptimizetheefficiencyofmotordrive systemsifsignificantenergysavingsaretobeobtained.The inductionmotor(IM),especiallythesquirrel-cagetype,iswidely used in electrical drives and is responsible for most of the energy consumed by electric motors [5]. TheIMlossescanbeclassifiedasstatorcopperlosses,rotor copper losses, iron losses, stray losses andmechanical (friction + windage)losses.Themainlosses,about80%ofthetotallosses, are copper (stator + rotor) and iron losses. The focus of this paper isontheminimizationoftheselosseswhichwillbereferredto collectivelyaselectromagneticlosses. However,therearemany applicationswhichrequireadjustabletorqueand/orspeed.If theseparametersvaryfarfromtheratedoperatingpointcausesa notorious IM efficiency drop. Thisisduetotheimbalancebetweenironandcopperlosses. Underthesecircumstances,itisnotpossibletoincreasethe efficiencybytheimprovementofthemachinedesign.Although differentvariableshavebeencontrolled,inallthesemethodsthe efficiencyimprovementisalwaysachievedbyindirectly controlling the balance between copper and iron losses. Themechanismthatpermitstheelectromagneticloss minimization acts as follows: Electromagnetic torque is proportional to the vector product of the rotor magneticflux and rotor current. It is thus possible to obtain thesametorquewithdifferentcombinationsoffluxandcurrent values.Foragiventorque,theironlosscanbeminimizedby usingtheminimumpossibleflux.Thisalsominimizesthestator copper loss component due to the magnetizing current. Ontheotherhand,tocreatetherequiredtorquewithless magnetizingflux,therotorcurrentmustbeincreasedby increasingthestatorcurrentand,consequently,thetotalcopper losses. Byaproperadjustmentofthemagneticflux,anappropriate balancebetweencopperandironlossescanbeachievedto minimizetheelectromagneticlosses.Almostallthemethods reportedforminimizingtheIMlosseshavebeendevelopedfor steady-stateoperationorveryslowlychangingconditions.This excludestheirusewhenthedynamicresponseisalsoimportant [5]. Inmanyapplicationsefficiencyoptimizationofinductionmotor (IM) which is the most used electrical motor presents an important factor of control especially for autonomous electrical traction. The very extensive use of induction motor implies that if losses in IM drivescanbereducedbyjustafewpercent,itwillhaveamajor impact on the total electrical energy consumption [5] - [6].In high dynamic performances control schemes used in industrial applications like vector control and direct torque control, the flux isusuallymaintainedconstantequaltoitsnominalvalue;inthis situationtheinductionmotorrunefficientlyaroundthenominal point [6]. When the load is reduced considerably, the efficiency is also greatly reduced and the electrical energy consumption is then highly affectedTosolvethisproblem,manyapproacheshavebeendevelopedin order to obtain a highly efficient IM drives as discussed above. In thiswork,theapproachusedisbasedongeneticalgorithm(GA) developed byJ.Hollandduringthe1960s.TheGAisbasicallya stochastic searching algorithm inspired by principle of the natural evolutionofspecies.Itiscapabletosolvingnonsmooth,non continuousandnon-differentiableproblemsforparallel computationtofindglobalornearglobaloptimalsolutions[7] comparisonbetweenconventionaloptimizationtechniquesand GA is presented in Table 1. Inthispaper,FuzzymodelidentificationthroughPSOis employedtominimizetheIMlossesinordertoevaluatethe optimalmagnetizingcurrent,thusmaximizingefficiency.Inthis study, the motor model includes iron losses. Simulation results are comparedwiththoseobtainedwiththeconventionalmethodand the proposed method. International Journal of Computer Applications (0975 8887)Volume 3 No.1, June 2010 7 2. METHODS OF LOSS REDUCTION AND CONTROL TECHNIQUES Losses in an IM constitute copper loss and core loss in stator and rotor,mechanicalloss,andstrayloadloss.Corelossandcopper lossdependonthemagneticandelectricloadingofthemachine and,therefore,arecontrollable.Thestrayloadlossdepends mainlyontheconstructionofthemotor(typeofstatorandrotor slots,lengthofoverhang,etc.)andalsoontheharmonicsinthe supply voltage. Usually, for a given motor and specified load, the sum of stray load loss and the mechanical loss do not exceed 30% of the total losses and may beassumed to remain constant. Thus, themotivationoflossminimizationistolookforanoptimum balance of the variable losses to make the total loss minimum. So far,effortsonlossminimizationareputintothreemajor directions:1)throughimproveddesignofthemotorand converter;2)bybettermanagementto operateagroup ofmotors inamoreefficientway;and3)byintroducingbettercontrol techniques.Therefore,investigationisfocusedonbettercontrol techniques to yield loss minimization [8]. 2.1 Loss Model Control (LMC)BasedontheIMlossmodeltheoptimumfluxiscomputed analytically.Withoutextrahardware,LMCcanbeconveniently realized.However,itmustneedanaccurateknowledgeofmotor parameters,whichchangeconsiderablywithtemperature, saturation, skin effect, etc [9].2.2 On-Line Power Measure Search ControlBased on minimum input power control, SC use particularsearch algorithms to find the optimum flux [10]-[11]. This approach does not require the knowledge ofmotor parameters. But the optimum fluxsearchtimeislongerthanthatofLMC.Inthesesearch methods,thefuzzylogicbasedsearchmethodisthemore successfulone.Buttheneedofsimulationcalculationtogetthe coefficientsoftheFLscalingfactors,nodoubt,willlimitthe applicationofthismethod.Unlikesuchsimulationmethod,the new gain derivative method presented by this paper, which makes full use of the results of LMC, is simple and effective. Moreover, new fuzzy sets are designed to get over the oscillations around the optimumflux.ThesimulationofIMverifiesthatthisstrategyis veryfastandhighlyprecise,andcanbeappliedforanysteady state of IM. 3. PROBLEM FORMULATION-MOTORLOSS MODEL 3.1 Losses of Induction MotorThe d-q model for a three phase IM in the synchronous frame can bewritten,whenselectingthestatorcurrents(isd,isq)andthe rotorfluxes(rd,rq)asstatevariablesas,[13].Thetotalloss equation is arrived from [3] & [13]. Where , ,andThe total losses of an IM consist of stator and rotor copper losses, corelossesPfeandmechanicallossesPm.Inthesteadystatethe stator and rotor copper losses are defined as follows: =(5) =(6) Thecorelossesincludingtheeddycurrentandhysteresislosses are given by: + ) (7) (8) Thecoefficientsofhysteresisandeddycurrentlossesmaybe expressed as kh and ke respectively which can be determined from standardno-loadtestdata,[13].Asareasonableapproximation, the mechanical losses are dependent on the rotor speed, [10]. They can be expressed by: (9) Where km is the mechanical loss coefficient. Asthestatorcurrentsisdandisqareregulatedandthemotoris controlledtobefieldorientedtotherotorflux,accordingtothe following relation: (10) Insteady,theoperatinglossesofthemachinecanbeexpressedas follows: (11) The motor torque can be expressed as(12) (13) 4. LOSS MINIMIZING STRATEGY 4.1 Control Block Diagram Isq IsqZ PIsd Z Z PP P PInternational Journal of Computer Applications (0975 8887)Volume 3 No.1, June 2010 8 Asimplifiedblockdiagramoftheoptimizationprocedureis depictedinFigure.1;itisimplementedinclassicalrotorflux orientedcontrol(FOC).InthisschemefuzzyPSOoptimization algorithmisproposed,wheretwophasecurrentsandtherotor speed are measured in order to calculate Figure 1.Block diagram of the optimization control system the electromagnetic torque and the magnetizing current (i) which enables us to express the total motor losses. 5. FUZZY LOGIC APPROACH The fuzzy logic is an aggregation of rules, based on the input state variablesconditionwithacorrespondingdesiredoutput.A mechanismmust exist to decide on which output,or combination ofdifferentoutputs,willbeusedsinceeachrulecould conceivablyresultinadifferentoutputaction.Fuzzylogic providesmachineryforcarryingoutapproximatereasoning processes when the available information is uncertain, incomplete or vague. The success of this methodology has been demonstrated inavarietyoffields.Severalfuzzylogicbasedefficiency controllers have been reported in literature, [9]-[10]. A fuzzy logic controlleressentiallyembedstheexperienceandintuitionofa human plant operator, and sometimes those of the designer of the plant, [13]. 5.1 Fuzzy Controller Design AccordingtotheoptimizationprinciplebasedonFigure.2,two inputvariablesareconsidered,thetorquecurrentcomponentIsq anditsvariationIsq.Theoutputofthefuzzycontrolleristhe statorcurrentcomponentIsdn,whichiscalculatedtominimize both copper and iron losses. The above inputs and output variation domains are limited and normalized as follows, [13]-[14]: Control rules are extracted and summarized in Table 1

Table 1.Fuzzy Rules (Z: Zero, P:Positive) Figure 2. Membership Functions for proposed scheme 6. GA OPTIMIZATION PROCEDURE TheGAwasintroducedbyJ.Hollandduringthe1960s.Itis known as a stochastic searching algorithm inspired by principle of the natural evolution of species.This tool is defined as stochastic optimizationtechniquebasedonthegeneticnaturalevolution mechanismofcreativebeings.Suchalgorithmisfoundtobea powerfulcomputationaltoolinseekingoptimumsandis considered as the most up-to-date product of artificial intelligence techniquesthatemulatethemechanicsofnaturalselectionand genetics. It explores, with coding parameter set, the workspace by meansofmechanismofreproduction,withthetargetof optimizingtheprocessselection.Thismechanismcomprises selection,crossoverandmutationoperations.Inthiscase,this optimizationprocedureconsistsofsearchingtheoptimum magnetizing current (flux) value for a given load torque by relying on GA. The loss equation [3] can be expressed as Figure 3. Flowchart for Efficiency Optimization by Gas Thisapproachrequirestheintroductionofanobjectivefunction (17)whichevaluateshowmuchgoodthefittedvaluesofthe magnetizing current are. From this function, a fitness that controls thereproductionprocessisderived.Theindividualsoftheinitial GApopulationareencodedinbinarystringswhereeach individual representing a parameter takes 10 bits. The input (load torque) and optimized output (I) are shown in the Figure 3. The criteriontoselectthebestindividualsforreproductionisthe objective(fitness)function.Byproceedinginthisway,the objectivefunctionadoptedforthisproblemistheIMtotallosses givenbyequation(17).Eachgenerationissubjectedtothe crossoverandmutationmechanisms.Thecrossoverconsistsin International Journal of Computer Applications (0975 8887)Volume 3 No.1, June 2010 9 randomlyselectingapositionalongparentsstringaswappingall binarydigitsfollowingthatposition.Themutationfollows crossoverandworksbyrandomlyselectingonestingandonebit location,changingthatstringsbitfrom1to0orviceversa.The probabilities of the crossover and mutation are set to 0.8 and 0.01 respectively.Theoptimized(I)isusedintheinductionmotor statorcontrollerforimprovingtheperformanceoftheinduction motor.7.FUZZYMODELIDENTIFICATION THROUGH PSO TheoriginofPSOisbestdescribedassociologicallyinspired, sinceitwasinitiallydevelopedasatoolbyReynoldsfor simulating the flight patterns of birds, which was mainly governed bythreemajorconcerns:collisionavoidance,velocitymatching andflockcentering[17].Likeevolutionarycomputation techniques,itusesapopulationofpotentialsolutionscalled particlesthatareflownthroughthehyperspace/search-space.In PSO, the particles have an adaptable velocity that determines their movementinthesearch-space.Eachparticlealsohasamemory andhenceitiscapableofrememberingthebestpositioninthe search-spaceever visited by it.The position corresponding to the bestfitnessisknownaspbestandtheoverallbestoutofallthe particlesinthepopulationiscalledgbest.Considerthatthe search-spaceisd-dimensionalandi-thparticleintheswarmcan berepresentedbyXi=(xi1,xi2,,xid)anditsvelocitycanbe representedbyanotherd-dimensionalvectorVi=(vi1,vi2,, vid).Letthebestpreviouslyvisitedpositionofthisparticlebe denotedbyPi=(pi1,pi2,,pid).Ifg-thparticleisthebest particleandtheiterationnumberisdenotedbythesuperscript, then the swarm is modified according to the Eqs. (18) and (19). (18) (19) where, w inertia weightc1 cognitive accelerationc2 social accelerationr1,r2randomnumbersuniformlydistributedintherange (0,1).TheparameterVmaxisthemaximumvelocityalongany dimension, which implies that, if the velocity along any dimension exceeds Vmax, it shall be clamped to this value. The inertia weight governshowmuchofthevelocityshouldberetainedfromthe previoustimestep.Generallytheinertiaweightisnotkeptfixed andisvariedasthealgorithmprogressessoastoimprove performance. Figure4. Depiction of position updates in particle swarm optimization for 2-D parameter space 7.1 Fuzzy Model Identification Problem The problem of fuzzy model identification includes the following issues:Selecting the type of fuzzy model Selecting the input and output variables for the model Identifyingthestructureofthefuzzymodel,whichincludesdeterminationofthenumberandtypesof membership functions for the input and output variables and the number of fuzzy rules Figure 5. Proposed Loss minimization algorithm Identifying the parameters of antecedent and consequent membership functions Identifyingtheconsequentparametersofthefuzzyrule base. The objective of optimization problem is to look for the values of thevariablesbeingoptimized,whichsatisfythedefined constraints, which maximizes or minimizes the fitness function. In this paper Mean Square Error (MSE) defined in Eq. (20) is used as fitness/objective function for rating the fuzzy model. (20) where, y(k) desired output(k) actual output of the modelZ number of data points taken for model validation A very important consideration is to completely represent a fuzzy system by a particle, and for this, all the needed information about the rule-base and membership functions is required to be specified through some encoding mechanism. It is also suggested to modify themembershipfunctionsandrule-basesimultaneously,since they are codependent in a fuzzy system. International Journal of Computer Applications (0975 8887)Volume 3 No.1, June 2010 10 Figure 6. Flowchart for the PSO algorithm 7.2 Algorithm of PSO Step 1 -Define fitness function Step 2 -Initialize the particles of the population according to the limits. Initialize parameters Wmax, Wmin,C1,C2and iter max Step 3 -Generate initial population of N particle with random position and velocities Step 4 calculate fitness: Evaluate the fitness values of current particle using the objective function Eq.(20)

Step 5 Update Pbest: Compare the fitness value of each particlewith its pbests. If the current value is better than pbest, then set pbest value to the current value. Step 6 - Update gbest: Compare the fitness value of each particle with its gbests. If the current value is better than pbest, then set pbest value to the current value. Step 7 -If the number of iterations reaches the maximum, then go to Step 8.Otherwise, go to Step 4. Step 8 -The particle that generates the latest gbest is the solution of the problem. Step 9 Stop 7.3 Encoding Mechanism Thefollowingconstraintsarefollowedbyeverymembership function of input and output variables. Particle Size=2mi-2 Thustheparticlesizeforrepresentingthemembershipfunctions of input and output variables for a Mamdani model is given by Eq. (21). Particle Size (for membership functions)= Particle Size(for rule base)= (22) Particle Size(for membership functions)=(23) 7.4FuzzyModelIdentificationthroughPSO: A Matlab Implementation Table2. List of Matlab functions i) Random Particle ii) Limit swarm iii) Limit Particle iv) Limit Membership Functions v) Limit rules vi) Get FIS vii) Calculate MSE Table 3. Strategy parameters for Identification of fuzzy model Swarm Size30 Iterations2500 C12 C12 W start(Inertia weight at the start of PSO run) 0.9 wend(Inertia weight at the end of PSO run) 0.3 V max75 8. RESULTS AND DISCUSSIONInordertofulfilltheIMenergysavingtaskbytheproposed approaches,anumericalsimulationhasbeencarriedoutusing Matlab/Simulinksoftware.Thenameplate-ratedcharacteristicsof theinductionmachinewithitsmodelparametersusedinthe simulationareshowninTable5.TheProposedfuzzyPSO optimizationprocedurehasledtoanoptimalvectorof magnetizingcurrent(i)valuesforseveralloads.Theseobtained results are then introduced in a lookup table and finally inserted in thecontrolblock.Asitisapparenttheefficiencyisgreatly improved over a wide range when using those methods compared withtheconventionalmethod.ThefuzzyPSOresultsaregreatly improvedparticularlyoverthelightloadregionthroughwhich habitually the efficiency values are relatively low. Define :Fitness function and constraints PSO Optimization searches for the fuzzy model Fitness function Termination is met End NO YES Objective : Fuzzy model identification YES International Journal of Computer Applications (0975 8887)Volume 3 No.1, June 2010 11 Figure 7. Matlab Simulation model of fuzzy controller based optimization of Induction motor Figure 8. Matlab Simulation of Fuzzy PSO based optimization of Induction motor Figure 9. Efficiency Vs Load for different optimization techniques International Journal of Computer Applications (0975 8887)Volume 3 No.1, June 2010 12 Table4. Comparison of efficiency values of different optimization techniques Load Without Optimization Proposed Approach Fuzzy logicGAFuzzy PSO 0.270.7884.4187.8195.21 0.471.1884.8488.8195.48 0.671.8885.3588.595.46 0.872.885.4388.6795.31 173.9785.5388.6395.33 Table5. Rating and Parameters of Induction Motor The equivalent core losses resistance is given by,: Where: A, B: constants charactering the iron losses. f: stator or rotor flux frequency 9. CONCLUSION Inthisstudy,Softcomputingapproacheshavebeentreatedand appliedtoimproveIMefficiency.Thefirstapproachisthe applicationoffuzzylogicandthesecondoneistheFuzzyPSO. Theadoptedstrategyconsistsofdecreasingtherotorfluxby adjustingthemagnetizingcurrentcomponentwithrespecttothe torquecurrentone.Theobtainedoptimizedresultsofthis investigationbythesetechniquesinconjunctionwiththoseof conventional methods are illustrated graphically in Figures 9.TheuseofPSOalgorithmforidentificationofoptimizedfuzzy modelfromoutputdatahasbeenpresented.Theyareofgreat interestsincetheIMefficiencyisimprovedoverawideload operatingrange.Distinctively,thoseyieldedfromthefuzzyPSO techniquearehighlyenhancedovertheusuallysuffersoflow efficiency values. From the presented study, one can conclude that thisIMefficiencyimprovementstrategy,implementedbyfuzzy PSO,isverypromisingandleadstoasignificantenergysaving under different load operating conditions. 10. REFERENCES [1]B.K.Bose,2002.PowerElectronicsandACDrives, Prentice Hall, USA. [2]D.E.Golberg,1989.GeneticAlgorithmsinSearch, Optimization and Machine Learning Reading, MA: Addison-Wisley, USA. [3]Z.Rouabah,F.Zidani,B.Abdelhadi,2008.Efficiency OptimizationofInductionMotorDriveusingFuzzyLogic andGeneticAlgorithms,IndustrialElectronics,ISIE,pp 737-742. [4]C.ThangaRaj,MemberIACSIT,S.P.Srivastava,and PramodAgarwal,April2009.EnergyEfficientControlof Three-PhaseInductionMotor-AReview,International Journal of Computer and Electrical Engineering, Vol. 1, No. 1, 1793-8198. [5]D.S.Kirischen,D.W.NovotyandT.A.Lipo,March 1987.OptimalEfficiencyControlofanInductionMotor Drive, IEEE Trans. On Energy Conversion, Vol. EC-2, No. 1, pp. 70-76. 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Appl Soc., pp. 544-552. [17]ArunKhosla,ShaktiKumar,,K.K.AGGARWAL,and Jagatpreet SINGH, 2006, Design of Fuzzy Models through Particle Swarm Optimization, Integrated Intelligent Systems for Engineering Design, IOS Press,Power P4kW Number of pairs of poles np2 Stator resistance Rs1.15, Rotor resistance Rr1.44 Total leakage factor 0.12, Mutual inductance M0.143 H Stator and rotor self-inductance = Lr 0.156 H Inertia J0.0240 SI Viscous friction coefficient f0.00 SI Rated Load torque TL7 N.m