改进小波结合BP网络的风力发电机故障诊断  

Fault Diagnosis of Wind Power Generator Based on Improved Wavelet and BP NN

作　　者：郭东杰[1] 王灵梅[1] 郭红龙 武卫红 韩西贵[1] 

机构地区：[1]山西大学工程学院,太原030013 [2]山西省电力科学研究院,太原030001

出　　处：《电力系统及其自动化学报》2012年第2期53-58,共6页Proceedings of the CSU-EPSA

基　　金：山西省自然科学基金项目(2009011021-2)

摘　　要：针对风力发电机早期故障时定子电流特征量难以提取的问题,提出了单子带重构改进小波变换结合BP神经网络的风力发电机故障诊断新方法。通过对风力发电机的定子电流进行单子带重构改进小波变换,消除了传统小波变换中的频率混叠现象;从小波变换后的子带信号中选取特征域、提取特征量作为BP神经网络的输入;在此基础上,结合BP神经网络的输入输出非线性映射能力,完成对故障的诊断和定位。经过仿真实验证实,该方法准确地实现了对风力发电机故障的诊断。It is hard to extract effective feature quantities from the stator currents when some early faults occur in the wind power generator.A novel algorithm that combining the wavelet transform improved by single-band reconstruction and BP network is proposed to solve this problem.By using the wavelet transform improved by single-band reconstruction to the stator currents of wind power generator,the frequency aliasing in the traditional wavelet transform is eliminated.Then,the characteristic field and features from each sub-band signals that are generated from the wavelet transform are selected as the input of BP neural network.On this basis,by using BP neural network which has the input-output nonlinear mapping ability,the diagnosis and location of the fault are completed.The simulated results show that the method can accurately diagnose the faults of wind turbine generator.

关 键 词：单子带重构改进小波变换 神经网络 风力发电机 故障诊断 定子电流 反向传播网络 

分 类 号：TM315[电气工程—电机]