Demagnetization Fault Diagnosis of Permanent Magnet Direct Drive Generator Based on Improved Residual Neural Network

Abstract

The permanent magnet direct drive wind turbine is the core equipment of the wind turbine. Barrier analysis ensures its safe and reliable operation. The demagnetization failure of the permanent magnet motor will occur because the wind energy equipment works under bad conditions. Taking the permanent magnet direct drive wind turbine as the research object, this paper uses ANSYS simulation software to build a two-dimensional electromagnetic field model of the generator to simulate the uniform demagnetization fault of the permanent magnet direct drive generator to obtain the back electromotive force waveform under different operating conditions and convert the back electromotive force waveform into images as input data for fault diagnosis of the improved residual neural network. Two kinds of materials are used to verify the effect of the diagnosis model, and the simulation results show that both materials can be used for demagnetizing fault diagnosis, which proves the effectiveness of the fault diagnosis strategy.