Abstract
The seriousness of air pollution appears to be the importance of wind energy as a non-polluting energy source. Today, the use of wind power has become a trend for new countries to develop new energy sources. Wind turbines are the key equipment for converting wind energy into electrical energy, the quality of the state directly affects the efficiency of wind power generation. Therefore, how to effectively diagnose the wind turbine drive system is the guarantee of wind power generation. This paper establishes a fault diagnosis method for wind turbine drive based on vibration characteristics, by wavelet packet decomposition of vibration signals. The feature extraction is carried out and back propagation neural network is used for classification research. Finally, the simulation results show that the recognition rate is over 90%, which verify effectiveness of the proposed method.
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