面向电力系统的多粒度隐患检测方法-豆柴文库

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面向电力系统的多粒度隐患检测方法
Title:Multi-GranularityFaultDetectionMethodforPowerSystems
Introduction:
Withtheincreasingcomplexityandscaleofpowersystems,ensuringtheirreliableandsafeoperationhasbecomeasignificantchallenge.Thedetectionandidentificationoffaultsorpotentialhazardsinpowersystemsarecrucialforpreventingsystemfailuresandminimizingpotentialrisks.Inrecentyears,therehasbeengrowinginterestindevelopingmulti-granularityfaultdetectionmethodstoimprovetheaccuracyandefficiencyofhazarddetection.Thispaperaimstodiscussvariousmulti-granularityfaultdetectionmethodsandtheirapplicationsinpowersystems.
1.OverviewofMulti-GranularityFaultDetection:
Multi-granularityfaultdetectionreferstotheutilizationofmultiplelevelsofgranularitytoanalyzeandidentifypotentialhazardsorfaultsinpowersystems.Itinvolvestheintegrationofvariousdatasources,suchashistoricalinformation,real-timemeasurements,andexpertknowledge,toprovideacomprehensiveunderstandingofthesystem'scondition.Byconsideringdifferentlevelsofgranularity,frommacroscopicsystem-wideanalysistomicroscopiccomponent-levelanalysis,multi-granularityfaultdetectionmethodscaneffectivelyidentifyandlocatepotentialhazards.
2.ClassificationofMulti-GranularityFaultDetectionMethods:
Thereareseveralapproachestoimplementmulti-granularityfaultdetectioninpowersystems.Thefollowingaresomecommonlyusedmethods:
2.1.MacroscopicAnalysis:
Macroscopicanalysisfocusesonsystem-widemonitoringandidentificationofabnormalpatternsorbehaviors.Variousstatisticaltechniques,suchasprincipalcomponentanalysis(PCA),wavelettransform,andclusteringalgorithms,areemployedtodetectfaultsbasedonglobalsystemparameters.Thisapproachhelpsindetectinglarge-scaleanomaliesthatmayindicatepotentialsystem-widehazards.
2.2.MesoscopicAnalysis:
Mesoscopicanalysisconcentratesontheevaluationofspecificsystemcomponentsorsubsystems.Itinvolvestheexaminationofintermediatelevelsofgranularitybetweenmacroscopicandmicroscopicanalysis.Techniqueslikefaulttreeanalysis(FTA),Bayesiannetworks,andfuzzylogicareappliedtoevaluatetheim