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
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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
