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基于随机森林算法的配电线路短路故障分类.docx

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基于随机森林算法的配电线路短路故障分类 Title:Short-CircuitFaultClassificationinPowerDistributionLinesBasedonRandomForestAlgorithm Introduction: Powerdistributionlinesarecriticalcomponentsofelectricalinfrastructure,responsiblefordeliveringelectricityfromtransmissionstationstoend-users.However,faultssuchasshort-circuitscanoccur,leadingtooutages,safetyhazards,anddamagetoequipment.Therefore,accurateandefficientfaultclassificationmethodsareessentialfortimelyresponseandtheeffectiveoperationofpowerdistributionsystems.Thispaperproposesashort-circuitfaultclassificationapproachbasedontheRandomForestalgorithm. 1.Background: 1.1Short-CircuitFaultsinPowerDistributionLines: Short-circuitfaultsoccurwhenalowimpedancepathisformedbetweentheconductorsinapowerdistributionline,resultinginexcessivecurrentflow.Thesefaultsareoftencausedbyequipmentfailures,insulationdeterioration,orexternalfactorssuchaslightningstrikes.Itiscrucialtopromptlydetectandclassifysuchfaultstominimizedowntimeandpreventfurtherdamage. 2.RelatedWork: 2.1TraditionalFaultClassificationMethods: Traditionalfaultclassificationmethodsoftenrelyonhandcraftedfeatureextractionandstatisticalanalysistechniques.However,thesemethodsmaybetime-consumingandrequireexpertknowledgetodevelopaccuratemodels.Moreover,theymaystruggletohandlecomplexfaultpatternsandvariationsinreal-worldscenarios. 2.2MachineLearningforFaultClassification: Machinelearningtechniqueshaveemergedaspowerfultoolsforfaultclassificationinpowersystems.Thesemethodscanautomaticallylearnfromdataandextractmeaningfulpatterns,enablingaccurateandefficientfaultclassification.TheRandomForestalgorithmisapopularmachinelearningalgorithmknownforitsrobustnessandabilitytohandlebothnumericalandcategoricalfeatures. 3.Methodology: 3.1DataCollectionandPreprocessing: Todevelopaneffectivefaultclassificationmodel,acomprehensivedatasetofpowerdistributionlinefaultsiscollected.Thisdatasetincludesvariousfaultscenarios,suchasphase-to-phaseandphase-to-groundfaults,withcorrespondingfeaturessuchasfaultlocation,faultcur