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1、EnergyandPowerEngineering,2012,4,529-538http://dx.doi.org/10.4236/epe.2012.46066PublishedOnlineNovember2012(http://www.SciRP.org/journal/epe)OnlineDiagnosisandMonitoringforPowerDistributionSystemAtefAlmashaqbeh,AoudaArfoaElectricalEngineeringDepartment,Ta
2、filaTechnicalUniversity,Tafila,JordanEmail:dr.atef_almashakbeh@yahoo.comReceivedOctober16,2012;revisedNovember14,2012;acceptedNovember26,2012ABSTRACTRecently,powerdistributionsystemisgettinglargerandmorecomplex.Itisverydifficultevenfortheexpertstodiagnosi
3、sandmonitoringtomadebestaction.Thismotivatedmanyresearcherstoinvestigatepowersystemsinefforttoimprovereliabilitybyfocusingonfaultdetectionandclassification.Therehavebeenmanystudiesonproblemsbuttheresultsarenotgoodenoughforapplyingtorealpowersystem.Inthisp
4、aper,anewprotectiverelayingframeworktodiagnosisandmonitoringfaultsinanelectricalpowerdistributionsystemwith.Thisworkwillextractfaultsignaturesbyusingellipsefitusingleastsquarescriterionduringfaultcondition.Byutilizingprincipalcomponentanalysismethods,this
5、systemwillidentify,classifyandlocalizeanyfaultinstantaneouslyKeywords:FaultDetectionandClassification;ProtectiveRelaying;PCA;PSCAD1.IntroductionFaultdetectionisafocalpointintheresearchofpowersystemsareasincetheestablishmentofelectricitytransmissionanddist
6、ributionsystems.Theobjectivesofapowersystemfaultanalysisistoprovideenoughinformationtounderstandthereasonsthatleadtoaninterruptionandto,assoonaspossible,restorethehandoverofpower,andperhapsminimizefutureoccurrencesifpossibleatall[1].Severaltechniquesaread
7、optedforpatternrecognitionofgeneratingthehighfrequencysignalsArtificialNeuralNetwork(ANN)andWaveletsamongotherpowerfulpatternrecognitionandclassificationtools.ANNbasedalgorithmsdependonidentifyingthedifferentpatternsofsystemvariablesusingimpedanceinformat
8、ionANNisthattheresolutionisnotefficientsinceitcanbeaverysparsenetworkwiththeneedforlargesizetrainingdataaddinganadditionalburdenonitscomputationalcomplexity[2-4].Waveletstransformisadoptedtodiscriminatethefaultstype
