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1、EnergyPowerEngineering20124529538:dx.doi.g10.4236epe.2012.46066PublishedOnlineNovember2012(:www.SciRP.gjournalepe)OnlineDiagnosisMonitingfPowerDistributionSystemAtefAlmashaqbehAoudaArfoaElectricalEngineeringDepartmentTaf

2、ilaTechnicalUniversityTafilaJdanEmail:dr.atef_almashakbeh@ReceivedOctober162012revisedNovember142012acceptedNovember262012ABSTRACTRecentlypowerdistributionsystemisgettinglargermecomplex.Itisverydifficultevenftheexpertsto

3、diagnosismonitingtomadebestaction.Thismotivatedmanyresearcherstoinvestigatepowersystemsineffttoimprovereliabilitybyfocusingonfaultdetectionclassification.Therehavebeenmanystudiesonproblemsbuttheresultsarenotgoodenoughfap

4、plyingtorealpowersystem.Inthispaperanewprotectiverelayingframewktodiagnosismonitingfaultsinanelectricalpowerdistributionsystemwith.Thiswkwillextractfaultsignaturesbyusingellipsefitusingleastsquarescriterionduringfaultcon

5、dition.ByutilizingprincipalcomponentanalysismethodsthissystemwillidentifyclassifylocalizeanyfaultinstantaneouslyKeywds:FaultDetectionClassificationProtectiveRelayingPCAPSCAD1.IntroductionFaultdetectionisafocalpointinther

6、esearchofpowersystemsareasincetheestablishmentofelectricitytransmissiondistributionsystems.Theobjectivesofapowersystemfaultanalysisistoprovideenoughinfmationtounderstthereasonsthatleadtoaninterruptiontoassoonaspossiblere

7、stethehoverofpowerperhapsminimizefutureoccurrencesifpossibleatall[1].SeveraltechniquesareadoptedfpatternrecognitionofgeneratingthehighfrequencysignalsArtificialNeuralwk(ANN)Waveletsamongotherpowerfulpatternrecognitioncla

8、ssificationtools.ANNbasedalgithmsdependonidentifyingthedifferentpatternsofsystemvariablesusingimpedanceinfmationANNisthattheresolutionisnotefficientsinceitcanbeaverysparsewkwiththeneedflargesizetrainingdataaddinganadditi

9、onalburdenonitscomputationalcomplexity[24].Waveletstransfmisadoptedtodiscriminatethefaultstypefromthemagizinginrushcurrent[5].OthersincpatedwavelettransfmwithothermethodssuchasProbabilisticNeuralwk(PNN)adaptiveresonancet

10、heyadaptiveneuralfuzzyinferencesystemsupptvectmachines[610].FuzzylogicwasalsocombinedwithdiscreteFouriertransfmadaptiveresonancetheyprinciplesofestimationindependentcomponentanalysistoenhanceperfmance[9].Unftunatelymosto

11、ftheavailabletoolsffaultdetectionclassificationarenotefficientarenotinvestigatedfrealtimeimplementationthereisaneedfnewalgithmsextractiondatareductioninlargedatasets[9].TypicallyPCAisutilizedistoreducethedimensionalityof

12、adatasetinwhichthereisalargenumberofinterrelatedvariableswhilethecurrentvariationinthedatasetismaintainedasmuchaspossible[9].Theprincipalcomponents(PCs)arecalculatedusingthecovariancematrixafterasimplenmalizationprocedur

13、e.AfterellipsefittingweapplythePCAusingfollowingsteps:Step1:GetdatafromfittingellipseStep2:SubtractthemeanStep3:CalculatethecovariancematrixStep4:CalculatetheeigenvectseigenvaluesoftencovarianceMatrixStep5:Choosingcompon

14、entsfmingafeaturevect.InfactitturnsoutthattheeigenvectwiththehighesteigenvalueistheprinciplecomponentofthedatainFigure2afterapplyingPCAinfittedellipseduringfaultconditiontheeigenvectwiththelargeseigenvaluewastheonethatpo

15、inteddownthedleofthedata.Itisthemostsignificantrelationshipbetweenthedatadimensions..WenotetheangleofprincipalcomponentwillbeauniquedistinguishedasshowninFigure3.Theclassificationprocessofafaultisdividedintotwostagesthef

16、irstistheprefaultprocedureusingallsignaturesgeneratedpritotestingtoenfcetheirprojectionsontotheprincipalcomponentsspacecalculatedtheprinciplecomponenthealthyangle(PCHA).Thesecondstagesisthetestingprocessduringfaultcondit

17、ionarefollowedtoprojectthetestpatternontoPCAspacefollowedbymeasuringofthePrinciplecomponentfaultangle(PCFA).Thisminimumdistancewillidentifyamatchofapatterntoafaultnofaultatall.Thismethodusesonlycurrentvoltagesignalsmeasu

18、redbyrelayagentsateachbusofthewksectionstoidentifythetypeoffaultifitisathreelinestoground(3LG)singlelinetoground(LG)doublelinetoground(DLG)alinetoline(LL)fault.Italsodeterminesthephasesincludedinfaultthebuslineatwhichthe

19、faultoccurred.Ananalysisofallpossibletypesoffaultinthreephasesystemi.e.LGfaults(AGBGCG)LLfaults(ABBCCA)DLGfaults(ABGBCGCAG)3LGfaults(ABCG)iscarriedout.Inthispapertheproposedalgorithmdeterminesthetypeoffaultfirstfinallyit

20、determinesthefaultlocation.ToidentifythefaulttypewenotethePCFAwithlessthancomparingwithPCHAfexampleifwehaveFaultAGwenotePCFAfphasealessthanPCHAfphaseaPCFAfphasebcarethesameasPCHAfphasebcalsoffaultACGthePCFAfphaseaclessth

21、anPCHAfphaseacbutPCFAfphasebisthesameofPCHAfphasebalsoflowimpedancefaultthedifferencebetweenPCFAPCHAisveryhighwillincreasedgraduallyatfaultedbusesthenwillbeincreasedafterfaultedbusesbutinhighimpedancefaultthedifferencebe

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