采用Fisher线性判别法提取GIS内部局部放电信号最优能量特征

Optimal Energy Features of Partial Discharge Signals in GIS Extracted by Fisher Linear Discriminant

采用二元树复小波变换(DT-CWT)对特高频局部放电(PD)信号进行多尺度分解,求解了复小波最优分解层数,提取了最优分解尺度下的特高频PD信号实部和虚部高频层小波能量,并采用Fisher线性判别方法对能量特征进行选择,最后进行PD类型辨识。识别结果表明:优选后的实部和虚部高频层小波能量特征可以有效识别4种典型绝缘缺陷,识别率均达到了92.5%及以上,且最优复小波能量(OCWEF)特征在PD类型辨识中具有更优的敏感性和识别效果。

The dual-tree complex wavelet transform(DT-CWT)is adopted to make a multi-scale decomposition of UHF partial discharge(PD)signals,and an optimal algorithm for solving DT-CWT decomposition is proposed.In addition,the optimal complex wavelet energy(OCWE)features are extracted from the high-layer real and imaginary parts of UHF PD signals after decomposed by DT-CWT,and the fisher linear discriminant method is adopted to select the energy features.Finally,the selected features are used for PD type recognition.The results show that the high-layer wavelet energy features can effectively recognize four typical insulation defects in GIS with a recognition accuracy reaching 94.5%or above.It is proved that the OCWE features are more suitable for PD recognition.