Fault Diagnosis Method of Power Electronic Equipment Based on Improved Resnet Neural Network

At present, it is difficult to extract features for fault diagnosis of power electronic equipment, and professional personnel are required for fault analysis, which consumes a lot of time and energy. In order to solve this problem, a new method is proposed in this paper, which adopts the hybrid method of one-dimensional convolutional neural network and Resnet network, and makes full use of the advantages of one-dimensional convolution module and residual module to achieve automatic and accurate extraction of fault features. The results show that the improved Resnet method can effectively solve the problem of power electronic fault diagnosis, which proves the effectiveness of the method, and greatly improves the efficiency of power electronic fault diagnosis. This method has practical application value in engineering.