基于改进PSO-LSSVM模型的变压器绕组热点温度预测-豆柴文库

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基于改进PSO-LSSVM模型的变压器绕组热点温度预测
Title:ImprovedPSO-LSSVMModelforPredictingTransformerWindingHotspotTemperature
Abstract:
Transformerwindinghotspottemperaturedescribesthecriticalpointoftransformeroperation,whichifexceeded,canleadtodecreasedlifespan,insulationfailure,andevencatastrophicdamage.Accuratepredictionofthehotspottemperatureisessentialformaintenanceplanningandimprovedoperationalsafety.ThispaperproposesanimprovedParticleSwarmOptimization(PSO)-LeastSquaresSupportVectorMachine(LSSVM)modelforpredictingtransformerwindinghotspottemperature.Theproposedmodeltakesintoaccountthecomplexrelationshipbetweenvariousfactorsinfluencingthetemperature,therebyenhancingpredictionaccuracy.Experimentalresultsindicatethattheproposedmodeloutperformsconventionalpredictionmodelsintermsofaccuracyandgeneralizationability,makingitaneffectivetoolforreal-timemonitoringandmanagementoftransformertemperature.
1.Introduction
Transformersplayacrucialroleinpowersystems,convertingelectricalenergyfromonevoltageleveltoanother.Withincreasingelectricitydemands,theefficiencyandreliabilityoftransformersareofprimeconcern.Transformerwindinghotspottemperatureisacriticalparameterthatdirectlyaffectsitslifespanandperformance.Accuratepredictionofhotspottemperatureenablesoptimaloperationplanning,maintenancescheduling,andultimately,improvedoperationalsafety.ThispaperproposesanimprovedPSO-LSSVMmodelforpredictingtransformerwindinghotspottemperature.
2.Background
2.1TransformerWindingHotspotTemperature
Transformerwindinghotspottemperatureisthehighesttemperatureamongallthewindingsandinsulationmaterials.Itisdeterminedbytheheatingeffectcausedbyelectricalcurrent,resistivelosses,andenvironmentalfactorssuchasambienttemperatureandcoolingconditions.Accuratepredictionofthehotspottemperaturecanidentifypotentialfailuresandpreventcatastrophicevents.
2.2ConventionalPredictionModels
Conventionalpredictionmodels,suchasregressionanalysisandartificialneuralnetworks(ANN),havebeenwidelyemployedfortemperatureprediction.However,thesemodelso