Demagnetization Fault Diagnosis Based on Feature Extraction and Stacking Ensemble Learning for Permanent Magnet Synchronous Generator

During the operation of a permanent magnet wind turbine, magnet demagnetization failure may occur, which directly affects the regular operation of the wind turbine and adversely affects wind power generation. This paper proposes a demagnetization fault diagnosis method for permanent magnet generators based on feature extraction and stacking integrated learning. A permanent magnet generator with a power of 25 kW was used to conduct a demagnetization fault simulation experiment. Collect the current signal of the generator and extract features such as time domain, frequency domain, entropy, and singular value. The different extracted features are trained through the Stacking integrated learning framework to realize pattern recognition of demagnetization faults and determine the operating status of the generator, thereby realizing demagnetization fault diagnosis of permanent magnet generators.