局部放电UHF脉冲的时频特征提取与聚类分析

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Abstract In this paper, four types of artificial defect models are used to generate typical PD sources, and laboratory PD experiments are performed to measure the UHF pulses. S transform(ST) is introduced to present a time-frequency analysis of partial discharge(PD) UHF pulses to explore PD sources separation. In the proposed method, ST is firstly applied to the UHF pulses of PD, and non-negative matrix factorization(NMF) is utilized to decompose the S transform amplitude matrix into a number of base vectors in frequency domain and location vectors in time domain. Then, a few parameters such as sharpness, sum of derivative, information entropy and sparsity are extracted from various base and location vectors, forming a PD feature space which can fully reflect the time- frequency information of the UHF Pulses. Finally, a fuzzy C means(FCM) algorithm is used to perform the clustering of extracted feature vectors. Clustering results of the experimental samples show that the UHF pulses from different sources can be effectively separated by the extracted time-frequency feature vectors. The highest successful clustering rate of 90.33% is obtained by the 10-dimensional feature vectors when NMF parameter r=2. In addition, ST can make better clustering accuracies than traditional Wigner-Ville distribution(WVD). The clustering performances of the proposed time- frequency features would be dramatically influenced when multiple refraction and reflection exists, and further investigations should be presented.

Key words： Partial discharge pulses clustering S transform non-negative matrix factorization fuzzy C means

Received: 16 September 2013 Published: 14 April 2015

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TM835

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	Wang Ke
	Liao Ruijin
	Wang Jiyu
	Yang Lijun
	Li Jian

Cite this article:

Wang Ke,Liao Ruijin,Wang Jiyu等. Time-Frequency Features Extraction and Clustering Analysis of Partial Discharge UHF Pulses[J]. Transactions of China Electrotechnical Society, 2015, 30(2): 211-219.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2015/V30/I2/211

[1] Liao Ruijin, Yang Lijun, Li Jian, et al. Aging condi- tion assessment of transformer oil-paper insulation model based on partial discharge analysis[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2011, 18(1): 303-311.
[2] 严家明, 廖瑞金, 杨丽君, 等. 油浸绝缘纸局部放电损伤产物分析[J]. 电工技术学报, 2011, 26(5): 184-191.
Yan Jiaming, Liao Ruijin, Yang Lijun, et al. Analysis of damage products of oil-impregnated insulation paper caused by partial discharge[J]. Transactions of China Electrotechnical Society, 2011, 26(5): 184-191.
[3] 杨丽君, 廖瑞金, 孙才新, 等. 油纸绝缘的局部放电特征量分析及危险等级评估方法研究[J]. 中国电机工程学报, 2011, 31(1): 123-130.
Yang Lijun, Liao Ruijin, Sun Caixin, et al. Partial discharge characteristics and risk assessment method for oil-paper[J]. Proceedings of the CSEE, 2011, 31(1): 123-130.
[4] Mazzetti C, Frattale Mascioli F M, Baldini M, et al. Partial discharge pattern recognition by neuro-fuzzy networks in heat-shrinkable joints and terminations of XLPE insulated distribution cables[J]. IEEE Transac- tions on Power Delivery, 2006, 13(4): 1035-1044.
[5] Wang M H. Partial discharge pattern recognition of current transformers using an ENN[J]. IEEE Transac- tions on Power Delivery, 2005, 20(3): 1984-1990．
[6] 汪可, 杨丽君, 廖瑞金, 等. 基于离散隐式马尔科夫模型的局部放电模式识别[J]. 电工技术学报, 2011, 26(8): 205-212.
Wang Ke, Yang Lijun, Liao Ruijin, et al. Partial discharge pattern recognition based on discrete hidden markov models[J]. Transactions of China Electrotech- nical Society, 2011, 26(8): 205-212.
[7] Cavallini A, Montanari G C, Puletti F, et al. A new methodology for the identification of PD in electrical apparatus: properties and applications[J]. IEEE Transac- tions on Dielectrics and Electrical Insulation, 2005, 12(2): 203-315.
[8] Contin A, Pastore S. Classification and separation of partial discharge signals by means of their auto-correla- tion function evaluation[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2009, 16(6): 1609-1622.
[9] Hao L, Lewin P L, Hunter J A, et al. Discrimination of multiple PD sources using wavelet decomposition and principal component analysis[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2011, 18(5): 1702-1711.
[10] Pinpart T, Judd M D. Differentiating between partial discharge sources using envelope comparison of ultra- high-frequency signals[J]. IET Science, Measurement and Technology, 2010, 4(5): 256-267.
[11] Tang J, Li W, Liu YL. Blind source separation of mixed PD signals produced by multiple defects in GIS[J]. IEEE Transactions on Power Delivery, 2010, 25(1): 170-176.
[12] Gao Wensheng, Ding Dengwei, Liu Weidong. Research on the typical partial discharge using the UHF detection method for GIS[J]. IEEE Transactions on Power Delivery, 2011, 26(4): 2621-2629.
[13] Chang C S, Jin J, Chang C, et al. Online source recognition of partial discharge for GIS insulated substations using independent component analysis[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2006, 13(4): 892-902.
[14] Abdel Galil T K, EI Hag A H, Gaouda A M, et al. De-noising of partial discharge signal using eigen- decomposition technique[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2008, 15(6): 1657-1662.
[15] Stockwell R G, Mansinha L, Lowe R P．Localization of the complex spectrum: the S transform[J]．IEEE Transactions on Signal Processing, 1996, 44(4): 998-1001．
[16] 郭灿新, 勇明, 徐敏骅, 等. S变换在电力电缆局部放电信号时频分析中的应用[J]. 电工技术学报, 2010, 25(11): 9-14. Guo Canxin, Yong Ming, Xu Minhua, et al. Applica- tion of S transform for time-frequency analysis of partial discharge signals in power cables[J]. Transac- tions of China Electrotechnical Society, 2010, 25(11): 9-14.
[17] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]．Nature, 1999, 401(6755): 788-791．
[18] 高新波. 模糊聚类分析及其应用[M]. 西安: 西安电子科技大学出版社, 2004.
[19] 杨丽君, 孙才新, 廖瑞金, 等. 油纸绝缘老化状态判别的局部放电特征量[J]. 电力系统自动化, 2007, 31(10): 55-60.
Yang Lijun, Sun Caixin, Liao Ruijin, et al. Partial discharge features applied in aging condition discri- mination of oil-paper insulation[J]. Automation of Electric Power Systems, 2007, 31(10): 55-60.
[20] 袁鹏, 司文荣, 李军浩, 等. 局部放电脉冲的Wigner时频分布特性研究[J]. 高压电器, 2008, 44(6): 524-526.
Yuan Peng, Si Wenrong, Li Junhao, et al. Time- frequency characteristics of partial discharge pulse with wigner-ville distributions[J]. High Voltage Apparatus, 2008, 44(6): 524-526.
[21] 司文荣, 李军浩, 袁鹏, 等. 局部放电脉冲的仿射类时频分布特性研究[J]. 仪器仪表学报, 2008, 29(10): 2168-2174.
Si Wenrong, Li Junhao, Yuan Peng, et al. Study on characteristic of affine class time-frequency distribu- tion for partial discharge pulse signal[J]. Chinese Journal of Scientific Instrument, 2008, 29(10): 2168- 2174.
[22] 任重, 董明, 任明, 等. 冲击电压下SF 6 气体在极不均匀场中局部放电的时频特征[J]. 西安交通大学学报, 2013, 47(8): 115-120.
Ren Zhong, Dong Ming, Ren Ming, et al. Time- frequency characteristics of partial discharge of SF 6 gas in an extremely uneven field under impulse voltage[J]. Journal of Xi’an Jiaotong University, 2013, 47(8): 115-120.