风力发电机转子绕组匝间短路故障相诊断方法

Diagnosis of Inter-turn Short Circuit Fault Phase of Rotor Winding of Wind Generator

提出一种双馈异步风力发电机转子绕组匝间短路故障相的诊断方法。这一方法对传统Elman神经网络进行改进,在接收层和输出层之间增加新权值。通过仿真试验,对改进前后Elman神经网络的训练结果进行对比,确认采用改进后的Elman神经网络可以在短时间内对双馈异步风力发电机转子绕组匝间短路的故障相进行诊断。

A method for diagnosing the inter-turn short circuit fault phase of the rotor winding of a DF asynchronous wind generator was proposed.This method improves the traditional Elman neural network,adding new weight between the receiving layer and the output layer.Through the simulation test,the training results of the Elman neural network before and after the improvement were compared,and it was confirmed that the improved Elman neural network can diagnose the inter-turn short circuit fault phase of the rotor winding of the DF asynchronous wind generator in a short time.