Design and Realization of Automatic Fault Diagnosis System of Wind Turbine based on LabVIEW

The wind power as a clean energy has been researched and developed by many countries. The onshore wind turbine has difficulty to secure additional location by the depletion of good location and the increase in civil complaint. Therefore, it is essential to advance to the sea. However, the mechanical and electrical allowance that components of wind turbine must withstand was increase in a corresponding degree. The possibility of failure was increased, and then the secondary damage by limited access caused additional costs by locating offshore. In order to diagnose fault in advance and ensure the reliability of large wind turbine located in the sea, we apply the VI technology and the software LabVIEW2012 to develop an automatic fault diagnosis and condition monitoring system. We collect measured signal data and extract feature information of the classified fault and normal signals pattern through wavelet analysis. The extracted feature information is used as the input of neural network learning. In this experiment, the stator current of doubly-fed induction generator was used as the input signal. The real-time signal analysis was made through wavelet transform. And then, the information of classified signal pattern is used to design an automatic fault diagnosis system by using neural network algorithm.