同步发电机励磁电源设计软件部分文献翻译.docx

同步发电机励磁电源设计软件部分文献翻译.docx

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同步发电机励磁电源设计软件部分文献翻译

南昌工程学院

2007级毕业（设计）论文文献翻译

机械与电气工程系（院）电气工程及其自动化专业

题目同步发电机励磁电源设计（软件部分）

学生姓名

班级07电气工程及其自动化

（1）班

学号

指导教师

日期2011年1月18日

南昌工程学院教务处订制

评语：

指导教师：

ExcitationControlofSelfExcitedInduction

GeneratorusingGeneticAlgorithmand

ArtificialNeuralNetwork

Abstract—Inductiongeneratorswhichmaybeoperatedingridorself-excitedmode,arefoundtobesuccessfulmachinesforwindenergyconversion.Outofthesetwoself-excitedmodeisgainingimportanceduetoitsabilitytoconvertthewindenergyintoelectricalenergyforlargevariationsinoperatingspeed.Howeverithasbeenfoundthatthesemachineexhibitsapoorvoltageregulation.Steady-stateanalysisofselfexcitedinductiongeneratorrevealsthatsuchgeneratorsarenotcapabletomaintaintheterminalvoltageandfrequencyintheabsenceofexpensivecontrollers.Inturnadditionofsuchcontrollersmayresultintoafallinpopularityofthismachineduetoitssimplicity.Anothersimplewaytocontroltheterminalvoltageisthroughexcitationcontrolusingseriescompensation.Inthispaperartificialintelligenttechniquesareusedtomodelthecontrolstrategyforproperreactivecompensationunderdifferentoperatingconditions.Geneticalgorithmalongwithartificialneuralnetworkhasbeenproposedtoestimatethevaluesofshuntandseriesexcitationcapacitancetomaintaintheterminalandloadvoltage.Simulatedresultsasfoundusingproposedcontroltechniqueareverifiedusingexperimentalresultsonatestmachine.Simulatedresultsarefoundtobeincloseagreementwithexperimentalresults.

Keywords—ArtificialNeuralNetwork,GeneticAlgorithm,SelfExcitedInductionGenerator,WindEnergyGeneration.

I.INTRODUCTION

Mostoftheelectricalpowergenerationacrosstheworldisduetotheuseoffossilfuel,whicharelimitedinreserve.Thesemayvanishfromearthifcontinuedtobeusedatthesamepace.Arapiddepletionoffossilfuelsaswellasafastgrowingpowerdemandhaspressurizedthescientiststothinkaboutnonconventionalsourcesofenergysuchaswindenergy,solarenergy,tidalenergy,etc.Useofsuchsourcesmaybehelpfultoretainourresourcesoffossilfuelforsomeadditionalyears.Scientistshaveobservedthatoutofallpotentialnonconventionalsourceswindenergyseemstobemoreattractiveandviable.Itisobservedthatwindscarryenormousamountofenergyandtheregionsinwhichstrongwindsprevailforasufficienttimeduringtheyearmayuseitforelectricalenergygeneration.Inadditiontothiswindenergygenerationprovidesacleanandpollutionfreeenvironmentanddoesnotleadtoglobalwarming.Furtherawindturbinegeneratormaybeaworthwhilepropositionforanisolatedremoteareaduetoabsenceofpowergrid.Therearemanyconsiderationsinthechoiceofgeneratorsforthewindturbineapplicationsandseveralviewsprevail.Howevermostoftheresearchersareinthefavourofinductiongeneratorsinself-excitedmodeduetoitsabilitytoconvertmechanicalpoweroverawiderangeofrotorspeeds.Inductiongeneratorsarealsopreferredduetoseveralotheradvantagessuchaslowcost,lessmaintenanceotheradvantagessuchaslowcost,lessmaintenanceandeasyoperation.Theself-excitedinductiongenerators（SEIG）arealsofoundsuitableforfewotherapplicationssuchastidalandminihydroelectricenergyconversion.Operationofinductiongeneratorinself-excitedmodeisusefulundervariablespeedoperationespeciallywhenwindspeedisfluctuatingwithinawiderange.ThereforeitbecomesthedutyofresearcherstoinvestigatethebehaviourofspecificproblemrelatedissuesofSEIG.TocomputethesteadystateperformanceofSEIG,researchersadopteddifferentmodels[1-12].Mainobservationwhichwaspointedoutincaseofself-excitedinductiongeneratorisitspoorvoltageregulation.Variousregulatingschemeswereproposedbyresearchpersons[13-25]toovercomethisissue.Howeverithasbeenrealizedthatsuchschemesmakesthesystemcomplicatedandexpensive.Reference[26]foundtheseriescompensationasimpleandcheapalternativetosuchschemes.Manyresearchscholars[27-31]studiedtheeffectsofseriescompensationusingdifferenttechniques.Ithasbeenobservedthatduringtheanalysisthedegreeofpolynomialequationinunknownfrequencyincreasesduetothepresenceofseriescapacitor.

InthispaperGAhasbeenproposedtoestimatethegeneratedfrequency,shuntandseriescapacitancefordifferentoperatingspeedsandwithdifferentloadconditions.AcontrolstrategyusingGAandANNcontrollerisproposedtocontroltheterminalandloadvoltageofSEIG.Simulatedresultshavebeenverifiedusingexperimentalobservationonatestmachine.

II.ARTIFICIALINTELLIGENTTECHNIQUES

A.ArtificialNeuralNetworks

Artificialneuralnetworks（ANN）[32-34],alsocalledparalleldistributedprocessingsystemsandconnectionist,aregenerallyusedforfunctionapproximation,nonlinearmodeling,systemidentification,patternassociation,patternclassificationetc.

Fig.1givestheANNarchitecture.

Thisnetworkhasanaturaltendencyforstoringexperimentalknowledgeandmakingitavailableforuse.Thisarchitecturewillnotcompletelybeconstrainedbytheproblemtobesolved.Thenumberofinputandoutputneuronsdependonthegivenproblembutthenumberofhiddenlayersandassociatedneuronswilldependonthedesigner.Inthispaper,thetwo-layerbackpropagationfeedforwardneuralnetworkhasbeenused.Atotalof5neuronsinhiddenlayer,twoneuronininputlayerandtwoneuroninoutputlayerhavebeenused.Thenetworkissetwith‘logsig’activationfunctionatthemiddlelayerand‘purelin’activationfunctionattheoutputlayer.Levenberg-Marquadrtmethodhasbeenadoptedforsupervisedlearning.Experimentaldatafortestmachine-1（seeAppendixI）andcomputeddatafromGAhavebeenusedfortrainingpurpose.

B.GeneticAlgorithm

Overthepastfewyears,manyresearchershavebeenpayingattentiontoreal-codedevolutionaryalgorithms,particularlyforsolvingreal-worldoptimizationproblems.GeneticAlgorithm（GA）[35]isoneofthem.Sincetheperformancevariablesevaluation（,CashandCse）inSEIGmaytakeanyrealnumber,therefore,inthispaperareal-codedgeneticalgorithmhasbeenusedtoinvestigatetheperformance.Inareal-codedGA,variablesarecodedinrealnumbersitself[36].GAoperatorsaredirectlyappliedontherealnumbers（,CshandCse）ThreemainoperatorsresponsiblefortheworkingoftheGAsarereproduction,crossover,andmutation.Reproductionoperatorallowshighlyproductivestringstoliveandreproduce,wheretheproductivityofanindividualisdefinedasastring’snon-negativeobjectivefunctionvalue.Therearemanywaystoachieveeffectivereproduction.Here,tournamentselectionisusedinsteadofroulette-wheelselection,whichisgenerallyused.Highervaluesoftournamentsizegivehigherselectionpressure,makingtheconvergencefaster.Thesecondoperator,crossover,exchangesgeneticinformation.Thestudyrevealsthatanumberofcrossoveroperatorssuchasblendcrossover（BLX）,simulatedbinarycrossover（SBX）,unimodalnormaldistributioncrossover（UNDX）,simplexcrossover（SPX）arecommonlyused.Adetailedstudyofsuchoperatorscanbefoundin[37-40].Hereparentcentricrecombinationoperator（PCX）hasbeenused,asthisoperatorassignsmoreprobabilityforanoffspringtoremainclosertotheparentsthanawayfromparents.Thesearchpowerofthiscrossoverisbetterthanthesimplecrossoveri.e.localorbroadersearchcanbedone.Differentmutationoperatorsareusedbasedonthecodingofthevariable.Sincecontinuousvariablesarecodeddirectly,thealgorithmisflexibleinnature.AsPCXandthereal-codedmutationoperatorshavebeenusedtohaveasearchpowersimilartotheircounterparts,theoverallalgorithmperformsbetterthanthebinary-codedGAs.ModifiedGA[35]processesfastconvergenceincomparisontoconventionalGAasreportedin[41].

III.MODELINGFORSTEADYSTATEANALYSIS

Thesteady-stateoperationoftheself-excitedgeneratorwithseriesandshuntcapacitorsmaybeanalyzedbyusingtheequivalentcircuitrepresentationasshowninFig.2.

Fig.2.Perphaseequivalentcircuitrepresentationfortwo-capacitorself-excitedinductiongenerator.

A.SelectionofCshatNoLoad

GAhasbeenusedtofindtheshuntcapacitanceatdifferentspeedstomaintaintheratedvoltageacrossthestatorterminalsatnoload.Thefitnessfunction（FF）usedhereis,

Fig.2.Perphaseequivalentcircuitrepresentationfortwo-capacitorself-excitedinductiongenerator.

Inthiscircuitmodelallparametersareassumedtobeindependentofsaturationexceptformagnetizingreactance.Thecorelosseshavebeenignored.ThenetworkabovecanbefurthertransformedintoFig.3.

Where,

Forgivenvalueofoperatingspeedandshuntcapacitanceascalculatedabove,GAmaybeusedtofindtheoptimumvaluesofgeneratedfrequency,aandseriescapacitanceCseforagivenloadimpedanceandcorrespondingtooperatingspeed.

Thiswillleadtotheestimationof‘a’and‘Cse’throughGAforconstantvoltageforanyoperatingspeed‘b’andloadresistance‘R’.

Fig.4.Variationofseriescapacitancewithspeedatdifferentloadsforconstantloadvoltage.

Fig4showsthevariationofcomputedvaluesofCsewithoperatingspeedfordifferentvaluesofloadresistance.Itisobservedthatwithanincreaseinspeed,initiallythevalueofseriescapacitanceCsedecreasesandafterattainingaminimumvalueitstartsincreasing.Thesecharacteristicsarealsousefultodeterminetheoperatingspeedforanyloadresul