基于小波包神经网络的整流电路晶闸管故障识别  

Thyristor fault identification of rectifier circuit based on wavelet packet and neural network

作　　者：马立新[1] 范丽君[1] 

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《能源研究与信息》2016年第1期45-50,共6页Energy Research and Information

摘　　要：在电力能效监控管理系统中,提出了基于小波包的特征提取和BP(back propagation)神经网络相结合的方法,对三相整流电路中故障晶闸管位置进行诊断和识别.根据整流电路原理,对22种故障情况分别进行编码.建立三相整流电路故障模型,采用小波包分解的方法,对直流端输出电压的采样数据进行特征提取,构建特征向量,作为BP神经网络的训练样本,将对应故障的编码作为网络输出,用简化的训练好的神经网络即可以实现整流电路的故障位置识别.仿真结果证明,采用小波包特征提取,作为神经网络训练样本,既可以简化神经网络训练结构,又可以准确实现故障定位识别.研究具有很大的工程实践意义.In the power energy efficiency management system,the feature extraction based on wavelet packet combining with back propagation（BP） neural network was proposed and applied to thyristor fault diagnosis and identification in the three-phase rectifier circuit. According to the principles of rectifier circuit, 22 kinds of fault were encoded respectively. The fault model of three-phase rectifier circuit was set up. Using the wavelet packet decomposition method, feature extraction of the DC output voltage was conducted to construct the feature vectors, which was saved as training samples of BP neural network. The corresponding fault codes were used as the network output. This simplified trained neural network could recognize the fault position of the rectifier circuit. The simulation results showed that the wavelet packet feature extraction, used as the neural network training sample, not only simplified the structure of neural network training, but also located the fault thyristor accurately. It indicated the engineering significance.

关 键 词：电力能效测评 小波包 特征向量 神经网络 整流电路 故障识别 

分 类 号：TM461[电气工程—电器] TP183[自动化与计算机技术—控制理论与控制工程]