Study on Integrated Optimization Method Applied in Fault Diagnosis of Wind Turbines

A sort of the genetic algorithm BP(GA-BP) neural network method was put to heighten the reliability of fault diagnosis in wind turbines with the wavelet transform.With the wavelet transform improved by single sub-band reconstruction, the signals of the stator current of the wind generator were resolved and reconstructed to extract the precise characteristic quantity.The stable weight and threshold were selected as the initial value of BP neural network by the selection, crossover,mutation operator and the global optimum capability of GA.The neural network was training repeatedly with the self-learning and precise optimum characteristic of BP network.The fault diagnosis of the wind generator were completed by the input-output nonlinear mapping ability of BP neural network.The algorithm comparison and real case analysis show that the algorithm has a good practicability in the fault diagnosis of the wind generator.