Fault diagnosis of three-level inverter based on convolutional neural network and support vector machine

Abstract

ABSTRACTDue to the strong nonlinearity and high complexity of NPC three-level inverter system, the model-based method is difficult to be used for open-circuit fault diagnosis of power switches. A fault diagnosis method (CNN-SVM) based on the combination of convolutional neural network (CNN) and support vector machine (SVM) is proposed. The data fusion method is used to integrate the output voltage characteristics of the inverter. The connection between data before and after is increased by it into a grayscale map. CNN is used to obtain the integrated voltage-related features, and SVM is used to classify the obtained features and then judge whether the fault occurs and the location of the fault. The experimental results show that the accuracy of the CNN-SVM model for inverter fault diagnosis is more than 96%, and it has high processing speed and strong generalization ability.CO EDITOR-IN-CHIEF: Yuan, Shyan-MingASSOCIATE EDITOR: Sun, Hung-MinKEYWORDS: Convolutional neural networksupport vector machinefault diagnosisthree-level inverter Nomenclature aandb=The size of the input feature mapa′andb′=The size of the new convolutional layerai=The fraction of output iβ=The biasdown()=The down sampling functionf()=The activation functionm=The size of the convolution kernelM=The set of input feature mapsl=The current convolution layer pi=The specified discrete probability distributiontn=Represents a nonlinear mappingw=The weight of the convolution kernelω=Denotes the weight vectorxjl=The output of the layerxn=The training datayn=Corresponding labelsεn=A slack variableDisclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by Central University Basic Research Fund of China under Grant [2018QNA09].