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

doi:10.11930/j.issn.1004-9649.201806108

中国电力 ›› 2019, Vol. 52 ›› Issue (9): 93-101.DOI: 10.11930/j.issn.1004-9649.201806108

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

田宇¹, 罗沙¹, 李宾宾^1,2, 孙文³

1. 国网安徽省电力有限公司, 安徽合肥 230022;
2. 国网安徽省电力有限力公司电力科学研究院, 安徽合肥 230022;
3. 国网电力科学研究院, 江苏南京 211000

收稿日期:2018-06-27 修回日期:2019-02-27 出版日期:2019-09-05 发布日期:2019-09-19
作者简介:田宇(1976-),男,高级工程师,从事电网变电设备检修与带电检测技术研究工作,E-mail:tiany_1976@126.com
基金资助:
国家自然科学基金资助项目（51537009）；国网安徽省电力有限公司科技项目（52120016001U）。

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

TIAN Yu¹, LUO Sha¹, LI Binbin^1,2, HU Yong³

1. State Grid Anhui Electric Power Co., Ltd., Hefei 230022, China;
2. Power Research Institute of Anhui Electric Power Co., Ltd., Hefei 230022, China;
3. State Grid Electric Power Research Institute, Nanjing 210000, China

Received:2018-06-27 Revised:2019-02-27 Online:2019-09-05 Published:2019-09-19
Supported by:
This work is supported by National Natural Science Foundation of China (No.51537009) and Science and Technology Project of Anhui Electric Power Co., Ltd., of SGCC (No.52120016001U).

摘要/Abstract

摘要： 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。

关键词: GIS, 局部放电, 能量特征, Fisher线性判别, 特征选择, 高电压测量技术

Abstract: 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.

Key words: gas insulated switchgear, partial discharge, energy features, fisher linear discriminant, features selection, high voltage measurement technology

中图分类号:

TM835

田宇, 罗沙, 李宾宾, 孙文. 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[J]. 中国电力, 2019, 52(9): 93-101.

TIAN Yu, LUO Sha, LI Binbin, HU Yong. Optimal Energy Features of Partial Discharge Signals in GIS Extracted by Fisher Linear Discriminant[J]. Electric Power, 2019, 52(9): 93-101.

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https://www.electricpower.com.cn/CN/Y2019/V52/I9/93

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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

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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

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