基于RBF神經網絡的永磁同步電機匝間短路故障診斷方法

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中圖分類號：TP277 文獻標志碼：A 文章編號：2095-2945(2025)14-0026-0

Abstract:Inter-tumshort circuitfaultsareoneof themostcommonandserious faultsthatoccrinpermanent magnet synchronousmotors (PMSM).Motorfaultdiagnosistechnologyisanimportantmeanstoimprovemotorreliabityandreducefault loses.Therefore,thispaperproposesamethodbasedonRBFneuralnetworktodiagnoseinter-turnshortcircuitfaultsin permanentmagnetsynchronousmotors.First,afiteelementmodelof inter-turnshortcircuitfaultof permanentmagnet synchronousmotorisestablished.Themotorwindingisdividedintomultiplesub-windings，andthetwoendsofthesubwindingsareconectedinparaleltosimulateinter-turnshortcircuitfaults.Secondly，theestablishedfiniteelementmodelis usedtosimulatethemotorperfomanceunderdiferentfaultdegres.Thepaperanalyzesandextractsfaultcharacteristicsfrom motortorque，phasevoltage,andphasecurent.Finally,afaultdiagnosissystem isestablishedusingRBFneuralnetwork.Ithas benverifiedthat theproposed faultdiagnosis methodcan diagnose diffrent degreesof inter-turn short circuits.

Keywords:permanentmagnetsynchronousmotor;inter-tunshortcircuitfault；faultdegree；faultfeatureextraction;RBF neural network

永磁同步電機(PMSM)具有結構簡單、體積小、重量輕和可靠性高等優(yōu)點-2，被廣泛應用于電動汽車、風力發(fā)電等領域[3-4。（剩余9267字）