A fault diagnosis method for DC/DC converter based on WOA neural network

Aiming at the problem that the traditional fault diagnosis method of DC/DC converter is single and not accurate, the fault features are extracted by wavelet packet transform, and the fault diagnosis model of DC/DC converter based on neural network is constructed by using whale optimization algorithm. By building a flyback DC/DC converter test platform, 70 sets of output voltage waveforms under the corresponding fault modes are collected, and the entropy value after wavelet packet transform is extracted as the fault feature input. Based on the whale optimization algorithm neural network fault diagnosis model, the fault diagnosis and analysis of DC/DC converter are realized. The experimental results show that the fault diagnosis rate of the whale optimization algorithm neural network model is more than 98%, which shows excellent fault diagnosis performance and effectiveness.