改进的小波神经网络算法对变流器的故障诊断方法

Fault diagnostic method for power converter based on wavelet neural network with improved algorithm

变流器是双馈风力发电系统中的枢纽设备,其运行可靠性直接关系到发电系统的安全与稳定。针对基于递推最小二乘(RLS)算法的离散小波神经网络(DWNN)存在收敛速度慢、收敛精度不高、搜索局部极小等不足,以变流器的电流为分析对象,提出一种采用变加权和变学习率改进算法的小波神经网络的变流器故障诊断方法。选择变流器电流作为离散小波神经网络训练及故障识别样本,对训练过程和仿真结果进行对比分析。实验结果表明:较之RLS算法,改进的小波神经网络故障诊断方法在故障识别准确率和收敛时间方面表现更优。

As one of the core equipments in doubly-fed induction wind power generation system,the operation reliability of power converters seriously influences the safety and stability of power generation system.Since some flaws exist in Wavelet Neural Network（WNN） based on Recursive Least Square（RLS） algorithm such as low convergence precision and rate,and searching space possessing local minima and oscillation.The authors proposed a modified algorithm for fault detection of diagnostic power converters,in which variable weight and alter learning coefficient were employed to resolve above problems.After the modified WNN was trained and the faults were recognized from practical current data,comparison and analysis were carried out in simulation.The experimental results demonstrate that the modified algorithm can provide higher diagnostic precision and require less convergence time than the RLS algorithm.