基于尺度-能量熵特征对的特高频局部放电辨识方法

对气体绝缘组合电器(GIS)进行局部放电(PD)检测,可以发现GIS内部早期绝缘缺陷和隐患,并预防绝缘事故发生。文中采用复小波分解(CWT)对GIS内部特高频(UHF)PD信号进行多尺度分解,分析了CWT能量熵(CWT-EE)随CWT尺度的变化规律,发现UHF PD信号信息主要分布在能量熵变化梯度较大的尺度下。为此,文中提取CWT-EE及其对应尺度,构建尺度-能量熵(SP-EE)特征对,既保留了PD信号能量特征信息,又保留了UHF PD信号小波尺度信息。最后,采用支持向量机(SVM)进行UHF PD类型辨识,结果表明:SP-EE特征对不但可以有效识别GIS内部4种典型绝缘缺陷,而且能够有效降低UHF PD信号分解层数和PD特征维数。

Pattern recognition of ultra-high frequency partial discharge by using scale parameters-energy entropy characteristic pairs

Early insulation defects and hidden dangers in gas insulated switchgear (GIS) can be found by partial discharge (PD) detection of GIS, and then the insulation accidents can be prevented. In this paper, the complex wavelet transform (CWT) is used to process the ultra-high frequency partial discharge (UHF PD) signal in GIS at different scales. The trend curves of CWT energy entropy (CWT-EE) under different decomposition scales are analyzed, and it is found that the PD feature information mainly distributed in the scales, in which the gradient of CWT-EE are big. Besides, The CWT-EE characteristics and their scales are extracted to the structure characteristic pairs for PD type identification, which contained not only the PD signals energy feature information, but also the wavelet scale information of UHF PD signals. Finally, the support vector machine (SVM) method is used to classify four typical defects UHF PD signals in GIS. The recognition results show that the characteristic pair can effectively identify four typical defects in GIS and obviously reduce the decomposition scales of UHF PD and the feature dimension.