The three-phase inverter is an important part of the motor drive system in an electric vehicle(EV).When a fault occurs,the fault sample size will be limited due to the short occurrence time,resulting in sample imbalance.To solve this problem,an inverter fault diagnosis method combining conditional generative adversarial network(CGAN)and convolutional neural network(CNN)is proposed in this paper.First,the phase current is taken as a fault sensitive signal,its frequency-domain characteristics are obtained by fast Fourier transform,and normalized preprocessing is carried out.Then,each sample is labeled and input into the CGAN model for countermeasure training to generate new samples in each fault mode.Finally,the CNN model is used to distinguish various fault modes of inverter.Through experimental research,it is found that the fault diagnosis accuracy based on CGAN-CNN can reach more than 98％,indicating that the proposed sample generation method is better than the traditional Smote and GAN methods.The results in this paper provide theoretical support for the intelligent operation and maintenance of new energy EVs.
维普
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