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

Abstract

ABSTRACT Due 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.