Separation of partial discharge mixing signals and type identification of defects in gas insulated switchgear based on fast independent component analysis algorithm

被引：0

作者：

Electric Power Research Institute, State Grid Shandong Power Company, Jinan 250002, China ^{[1
]}

不详 ^{[2
]}

不详 ^{[3
]}

不详 ^{[4
]}

机构：

[1] Electric Power Research Institute, State Grid Shandong Power Company

[2] State Grid of China Technology College

[3] School of Electrical Engineering, Shandong University

[4] State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University

来源：

Gaodianya Jishu | / 3卷 / 853-860期

关键词：

Fast independent component analysis; Gas insulated switchgear; Mixing signals separation; Multiple insulation defects; Partial discharge; Type identification of defects;

D O I：

10.13336/j.1003-6520.hve.2014.03.030

中图分类号：

学科分类号：

摘要：

In order to realize the signals separation and pattern recognition of mixing partial discharge(PD) that exist with multiple insulation defects in gas-insulated metal-enclosed switchgear(GIS), we presented a method based on fast independent component analysis (FastICA) algorithm. In detail, we separated single PD signals from mixing signals using the FastICA algorithm, then proposed a recognition strategy that additional pole-reversed and amplitude-normalized single insulation defect signals were used to train classifiers or the characteristics insensitive to signal polarity were used as classification characteristics. The calculation and analysis of an sample show that, the proposed method can separate PD mixing signals effectively and then recognize single insulation defects based on the separated single PD signals, and is insensitive to noise, fast in calculation and good in robustness.

引用

页码：853 / 860

页数：7

共 21 条

[1] Zhang X., Tang J., Tang J., Et al., Relationship between UHF signals and discharge magnitude from typical partial discharge defects in GIS, High Voltage Engineering, 38, 1, pp. 59-65, (2012)
[2] Hao L., Lewin P.L., Partial discharge source discrimination using a support vector machine, IEEE Transactions on Dielectric Electrical Insulation, 17, 1, pp. 189-197, (2010)
[3] Ding D., Gao W., Liu W., Insulation defects discrimination in GIS by fisher discriminant analysis of partial discharge, High Voltage Engineering, 39, 4, pp. 805-812, (2013)
[4] Ding D., Gao W., Liu W., Analysis on the typical partial discharge using UHF detection method for GIS, High Voltage Engineering, 37, 3, pp. 706-710, (2011)
[5] Tang J., Zhuo R., Wu M., Et al., Multiple-characteristics information-fusion decision diagnosis of partial discharge pattern of gas insulated switchgear, High Voltage Engineering, 39, 11, pp. 2581-2588, (2013)
[6] Yang A.L., Judd M.D., Recognising multiple partial discharge sources in power transformers by wavelet analysis of UHF signals, IEE Proceedings Science Measurement and Technology, 150, 3, pp. 119-127, (2003)
[7] Tang J., Meng Q., Wang C., Et al., Blind separation of partial discharge mixing signals, High Voltage Engineering, 36, 4, pp. 821-827, (2010)
[8] Jutten C., Herault J., Blind separation of sources, Part I: An adaptive algorithm based on neuromimatic architecture, Signal Processing, 24, 1, pp. 1-10, (1991)
[9] Xie Q., Zhang L., Cheng S., Et al., Application of the fast ICA algorithm to PD ultrasonic array signal de-noising, Proceedings of the CSEE, 32, 18, pp. 160-166, (2012)
[10] Wang B., Wang N., Jiang Y., Et al., Harmonic detection based on dynamic independent component analysis, Power System Protection and Control, 39, 2, pp. 40-44, (2011)

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