Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Generator for Wind Turbine Based on ResNet-50 Transfer Learning Model

This paper utilizes Convolutional Neural Networks (CNN) with Residual Networks (ResNet) to identify and classify demagnetization faults in images. Firstly, we collected current feature datasets corresponding to two types of permanent magnet wind turbines, namely 25 kW and 2 MW, representing the source and target domains. Using the source domain dataset, we trained a ResNet50 model specifically for demagnetization fault diagnosis of the 25 kW wind turbine. Subsequently, we applied a network-based transfer learning approach to adapt this model to the demagnetization fault diagnosis for the 2 MW wind turbine, completing the fine-tuning training required for transfer learning. Compared to models without transfer learning, this method significantly improves the accuracy and efficiency of demagnetization fault diagnosis.