英文文献翻译.docx

英文文献翻译.docx

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英文文献翻译

学校代码：

10128

学号：

200910202061

本科毕业设计英文文献翻译

（

题目：

ParticleSwarmOptimization-BasedMaximumPowerPointTrackingAlgorithmforWindEnergyConversionSystem

学生姓名：

赵晓翠

学院：

电力学院

系别：

自动化系

专业：

自动化

班级：

自动化09-2

指导教师：

崔啸鸣讲师

二〇一三年六月

2012IEEEInternationalConferenceonPowerandEnergy（PECon）,2-5December2012,KotaKinabaluSabah,Malaysia

Abstract

Duetothenatureofunpredictedwindspeed,determiningtheoptimalgeneratorspeedtoextractthemaximumavailablewindpoweratanywindspeedisessential.Therefore,itissignificanttoincludeanintelligentcontrollerthatcantrackthemaximumpeakregardlessofwindspeed.Thispaperdescribesthedesignanddevelopmentofparticleswarmoptimization（PSO）-basedmaximumpowerpointtracking（MPPT）algorithmtovariable-speedfixed-pitchwindturbines.Otherthantheelectricalpowersubjectedtomaximization,theproposedalgorithmdoesnotneedanyadditionalsensor.Inaddition,theMPPTalgorithmdoesnotrequireanypriorknowledgeofthewindenergysystem.Unliketheconventionalsearchoptimizationmethod,PSO-basedMPPTalgorithmproducesalmostnegligibleoscillationsatthemaximumpoweroncethetruepeakislocated.Inshort,theproposedMPPTissimple,flexible,accurateandefficientinmaximumwindpowertracking.Inthiswork,MATLAB/SimulinksimulationpackageisusedtosimulatetheperformanceoftheproposedMPPTalgorithm.

Keywords-particleswarmoptimization（PSO）;maximumpowerpointtracking（MPPT）;hill-climbsearching（HCS）;windenergysystem.

I.INTRODUCTION

Windenergysystemshavegainedtremendousattentionoverthepastdecadeasoneofthemostpromisingrenewableenergysourcesduetotheprobabledepletion,highcosts,andnegativeenvironmentalimpactsofconventionalenergysources.Windenergyisapollution-freeandinexhaustiblesource.Itismostlikelywindenergycouldbeoneofthepotentialsourcesofalternativeenergyforthenearfuture[1],[2].

Besidesbeingdependentonthewindspeed,theamountofmechanicalenergythatcanbeextractedfromthewindisgovernedbytheratiooftherotationalspeedtowindspeed.Thereisaspecificoptimalratioforeachwindturbine,whichiscalledtheoptimaltipspeedratio（TSR）oratwhichtheextractedpowerismaximum.Asthewindspeedisinstantaneouslyvarying,itisessentialfortherotationalspeedtobevariabletomaintaintheequalityoftheTSRtothedesirableoneatalltimes.

Inordertodeterminetheoptimaloperatingpointofthewindturbine,includingaMPPTalgorithmtothesystemisessential.MuchhasbeenwrittenonthetopicofMPPTalgorithms,especiallyforwindenergysystems.ThemanydifferenttechniquesforMPPTofwindenergyconversionsystemshavebeenreviewedanddiscussedindepthin[1],[2],[3].

AmongtheavailableMPPTalgorithms,hill-climbsearching（HCS）methodiswidelyusedinwindenergysystemstodeterminetheoptimaloperatingpointthatwillmaximizetheextractedenergy.Thismethodisbasedonperturbingacontrolvariableinsmallstep-sizeandobservingtheresultingchangesinthetargetfunctionuntiltheslopebecomeszero.Sinceitdoesnotrequirepriorknowledgeofthewindturbinescharacteristiccurve,HCSmethodisindependent,simple,andflexible.However,itfailstoreachthemaximumpowerpointsunderrapidwindvariationsifusedforlargeandmediuminertiawindturbines.Additionally,choosinganappropriatestep-sizeinMPPTalgorithmisnotaneasytask;thoughlargerstep-sizemeansafasterresponsebutitcreatesmoreoscillationsaroundthepeakpointwhichreducesthesystemefficiency;asmallerstep-sizeimprovesefficiencyyetreducestheconvergencespeed[4],[5],[6].

Inrecentyears,aswarmintelligence-basedalgorithm,aparticleswarmoptimization（PSO）,hasbeenusedacrossawiderangeofapplicationstolocatetheglobaloptimalsolutions.Itisacomputationalmethodthatoptimizesaproblembyiterativelytryingtoimproveacandidatesolutionwithregardtoagivenmeasureofquality.AsaMPPTalgorithm,PSOhasbeensuccessfullyappliedtoaphotovoltaic（PV）systembymanyauthors[7],[8],[9].However,ithasnotbeenusedinMPPTalgorithmforwindenergysystems,excepttoadaptthelearningratesintheback-propagationprocess,aimingatimprovingthelearningcapabilityoftheneuralnetwork-basedMPPT,asreportedin[10],[11].ComparedwithHCS,PSOincreasestheefficiencyoftherenewableenergyconversionsystem,whereitprovidesanadaptivestep-sizethatmayapproachzeroatthemaximumpoint,sothatitdoesnotproducetheoscillationsaroundthepeakpoint.

Inthiswork,thePSO-basedMPPTisusedtocontroltheduty-cycleoftheboostconverterthatinterfacesthewindturbinetotheload,inawaythatguaranteesextractingthemaximumavailablepowerfromthewind.Theremainderofthepaperisorganizedasfollows:

sectionIIdescribesthesimulatedwindenergysystem,insectionIII,thePSO-basedMPPTalgorithmisexplainedandappliedtothewindenergysystem.Followingthis,thesimulationresultsarepresentedanddiscussedandfinally,aconclusionisdrawn.

II.SYSTEMOVERVIEW

Fig.1illustratestheschematicdiagramofthesimulatedwindenergysystem.Thesystemsuppliesaresistiveloadandconsistsofawindturbinerotor,permanentmagnetsynchronousgenerator（PMSG）,rectifier,andaboostConverter.

Figure1AbriefblockdiagramoftheproposedPMSGwind-energysystem.

A.WindTurbineCharacteristics

Thewindturbineconvertsthewindenergyintomechanicalenergy,whichthenrunsageneratortocreateelectricalenergy.Themechanicalpowergeneratedbyawindturbinecanbeexpressedas[12]:

（1）

Wheretheairdensity（kg/m3）,Risistheturbinerotor（m）,Visthewindspeed（m/s）,andisthecoefficientofperformance.

Theturbinepowercoefficient（CP）describesthepowerextractionefficiencyofthewindturbine.Itisanonlinearfunctionofboththetipspeedratio（

）andthebladepitchangle（

）.Whileitsmaximumtheoreticalvalueisapproximately0.59,inpracticalityitliesbetween0.4and0.45[13].Thetipspeedratioisavariableexpressingtheratioofthelinearspeedofthebladetipstotherotationalspeedofthewindturbine,andcanbeexpressedby:

（2）

Manydifferentversionsoffittedequationsforhavebeenusedinpreviousstudies.Thispaperdefineditbasedonthefollowing[1]:

（3）

（4）

Duetotheassumptionofafixedpitchrotor,thispapersetstheangle（

）tozero.Hence,thecharacteristicsofmainlydependon.Byusing（3）,thetypicalversusλcurveisshowninFig.2.Asaforementioned,thereisanoptimumvalueofTSRthatleadstomaximumpowercoefficient.Theforthegivenwindturbineis8.131,whichcorrespondstoaof0.48.IftheTSRismaintainedconstantlyatitsoptimalvalue,thisensuresthattheenergyextractedisinitsmaximumoperatingpointtoo,asshowninFig.3.

Figure2Thecharacteristicofthepowercoefficientasafunctionofthetipspeedratio.

Figure3Characteristicsofturbinepowerasafunctionoftherotorspeedforaseriesofwindspeeds.

B.PermanentMagnetSynchronousGenerator（PMSG）

Amongtheelectricgenerators,PMSGispreferredduetoitshighefficiency,reliability,powerdensity;gearlessconstruction,lightweight,andself-excitationfeatures[1].ThispaperusesthesimplifiedmodelofthePMSGrepresentedin[14],atwhichthegeneratorandrectifierareconvenientlyrepresentedbyanequivalentcircuitviewedfromthedcsideoftherectifier.SuchanequivalentcircuitisshowninFig.4,wherethegeneratorismodeledbyitsopencircuitEMF（ew）anditsequivalentresistanceRW.

Figure4SimplifiedmodelofthePMSGandrectifier.

ThesourceEMF（ew）isproportionaltothegeneratorspeed（

）,andtheequivalentresistancehasavalueoftwicetheperphaseresistanceofthegenerator.

（5）

Neglectingdampingandfriction,themechanicaldynamicscanbereducedto:

（6）

（7）

whereKwisthegeneratorEMFconstant,Tmistheturbinemechanicaltorque,andTeisthegeneratorelectricaltorque.

C.BoostConverter

Toimprovethesimulationspeedsignificantlyandatthesametimekeepsthedesirableaccuracy.Inthispaper,ratherthansimulatingthedetailedmodeloftheboostconverter,theaveragedmodelrepresentedin[15]isusedinsimulation.Intheaveragedmodel,thepowerelectronicswitchanddiodearereplacedbythecombinationofcontrolledcurrentandvoltagesources.Itcansimulatetheconverterbehaviorpreciselyatlargetimescalewithreasonableaccuracy.

Figure5Nonlinearaveragedcircuitmodeloftheboostconverter.

III.PSO-BASEDMPPTALGORITHM

Particleswarmoptimization[9],[8],[16]isacomputationalmethodthatoptimizesaproblembyiterativelyimprovesacandidatesolutionwithregardtoagivenmeasureofquality.Itstartswithagroupofrandompotentialsolutions,whicharecalledparticles.Theseparticlesflyaroundinamultidimensionalsearchspacesearchingfortheoptimumsolutionbyadjustingtheirpositionsdependingontheirownexperienceaswellastheexperienceoftheotherparticles.ToberealizeasaMPPTalgorithm,theparticlepositioninPSOrepresentstheduty-cycle,thevelocityisthestep-sizeoftheduty-cycle,andtheobjectivefunctionismaximizingtheconverterpower.AsdepictedinFig.6,theparticlepositionandvelocityareupdatediterativelybasedonthefollowingtwoequations:

（8）

（9）

wherewisdefinedasthemomentumfactor,r1andr2aretworandomvaluesbetween（0,1）,c1andc2arepositiveconstantsknownasaccelerationconstants.

Figure6Conceptofmodificationofasearching