基于小波包分解的XLPE配电电缆局部放电波形特征提取与识别  

Feature Extraction and Pattern Recognition of Partial Discharge of XLPE Distribution Cable Based on Wavelet Packet Decomposition

作　　者：罗新[1] 牛海清[1] 胡日亮[2] 刘访[2] 吴炬卓[1] 

机构地区：[1]华南理工大学电力学院,广州510641 [2]广东电网公司东莞供电局,东莞523129

出　　处：《高压电器》2013年第11期110-116,122,共8页High Voltage Apparatus

摘　　要：XLPE中压电缆局部放电(partial discharge)带电检测获得的信号可能源于电缆本体、电缆终端头,也可能来自于与之连接的开关柜中的电晕放电或表面放电等等。由于不同来源的PD信号,对设备的危害不同,其判断标准也有所不同,故有必要对PD信号来源进行识别。笔者利用小波包分解技术对试验获得的大量PD波形数据进行去噪和特征提取。使用PD信号在不同尺度下的能量谱、Shannon熵、对数能量熵以及1.5阶标准熵组成4组特征向量;将提取出的特征向量分别作为BP神经网络分类器的输入,对PD信号进行识别,并得到以下结论:以提取的各特征向量对PD信号进行识别,平均识别率均在90%附近;能量谱、Shannon熵、对数能量熵对于表面放电的识别率相对较低,1.5阶标准熵对于表面放电识别率高但对于电缆本体PD信号识别率较低。提出使用能量谱和1.5阶标准熵组合特征向量对PD信号进行识别,效果优于单独使用各特征向量进行识别,识别率高达97%。The signal of online partial discharge（PD）detection of XLPE cable may come from the cable, its end joint, or the switchgear connected with it. PD from different source does different harm to electric equipment, and is recognized with different criteria. Therefore, it is necessary to recognize the source of PD. Based on wavelet packet decomposition, four eigenvectors, i.e. energy spectrum, Shannon entropy, logarithmic energy entropy, and 1.5-order norm entropy, are extracted in this paper. These eigenvectors are taken as the input of BP neural network to recognize PDs. The conclusions are as follows： 1）the recognition rates of BP neural network with respective four eigenvectors are all around 90%; 2）for surface discharge, the recognition rate with energy spectrum, Shannon entropy or logarithmic energy entropy is relatively low, while the recognition rate with 1.5-order norm entropy is high, however it is relatively low for cable PD. An improved method is proposed by taking the combined eigenvector of energy spectrum and 1.5-order norm entropy as the input of BP neural network, thus the recognition rate reaches 97%.

关 键 词：局部放电 小波包 特征向量 神经网络 模式识别 

分 类 号：TM855[电气工程—高电压与绝缘技术]