基于三维谱图混沌特征的GIS局部放电识别

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (796 KB)
输出: BibTeX | EndNote (RIS)

摘要气体绝缘电器（GIS）局部放电（PD）的故障诊断对于GIS的运行状态评估有着重要意义,传统模式识别方法局限于对描述PD谱图形态分布方面的特征进行分析与识别,缺乏对PD特征更全面、更深刻、更本质的分析研究,导致出现对某些类型放电识别率低等问题。针对这些问题,本文提出了一种基于混沌理论的GIS PD识别方法,连续采集100个工频周期的PD信号构成一个ϕ-v-n三维谱图样本F,以矩阵F的一列作为一个信号序列进行混沌分析,即计算对应同一相位信号序列的最大Lyapunov指数,获取36个最大Lyapunov指数在不同相位区间的分布特征作为不同相位下的局部放电混沌特征。实验结果表明,提取的混沌特征可实现对PD本质的深入挖掘,整体识别效果较好,特别是对于传统的统计特征识别方法难以区分的气隙类缺陷识别率很高,可作为统计特征识别方法的辅助方法加入到识别系统中,进一步提高识别准确率。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张晓星
	舒娜
	徐晓刚
	李鑫
	唐炬

关键词 ：局部放电, GIS, 支持向量机算法, 模式识别

Abstract：Fault diagnosis of gas-insulated switchgear (GIS) partial discharge (PD) is significant for the evaluation of GIS operation conditions. Traditional pattern recognition methods are limited to analysis and recognition the characteristics of PD spectra pattern distribution. Lack of a more comprehensive, more profound and more fundamental analysis on PD characteristics, which results in low recognition rate of a certain type of PD. Considering these problems, this paper proposes a GIS PD recognition method based on the chaos theory. Collect PD signals of 100 frequency cycle are collected continuously and an φ-v-n 3D spectra sample F matrix is formed. Taking one column of this matrix as a signal sequence and then conducting chaos analysis, that is, calculating the largest Lyapunov exponent corresponding to the same phase signal sequence and the distribution characteristics of 36 largest Lyapunov exponents in different phases as PD chaotic characteristics are obtained. The experimental results show that the extracted chaos features can reveal the essential of PD. The whole recognition is better and has high rate recognition of air gap defects that the traditional statistical feature recognition method cannot distinguish, which can be added to the recognition system as an auxiliary method of the statistical feature identification method, and further improve the accuracy of recognition.

Key words： Partial discharge GIS SVM pattern recognition

收稿日期: 2013-04-08 出版日期: 2015-03-23

PACS:

TM561

基金资助:国家重点基础研究发展计划（2009CB724506）和国家自然科学基金（50977095）资助项目

作者简介: 张晓星男,1972年生,博士,教授,研究方向为高压电气设备绝缘在线监测和故障诊断。舒娜女,1980年生,博士,研究方向为高压电气设备绝缘在线监测和故障诊断。

引用本文:

张晓星, 舒娜, 徐晓刚, 李鑫, 唐炬. 基于三维谱图混沌特征的GIS局部放电识别[J]. 电工技术学报, 2015, 30(1): 249-254. Zhang Xiaoxing, Shu Na, Xu Xiaogang, Li Xin, Tang Ju. GIS Partial Discharge Recognition Based on Chaos Features of the Three-Dimensional Spectra. Transactions of China Electrotechnical Society, 2015, 30(1): 249-254.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2015/V30/I1/249

[1] Satish L, Zaengl W S. Can fractal features be used for recognizing 3-D partial discharge patterns?[J]. IEEE Transactions on Electrical Insulation, 1995, 2: 352- 359.
[2] Gulski E. Application of modern PD detection tech- niques to fault recognition in the insulation of high voltage equipment[C]. 9th International Symposium on High Voltage Engineering, Graz, Austria, 1995: 5642.
[3] 张周胜, 马爱清, 盛戈皞, 等. 高压交联聚乙烯电缆局部放电脉冲的时频特性识别方法[J]. 高电压技术, 2011, 37(8): 1997-2003. Zhang Zhousheng, Ma Aiqing, Sheng Gehao. Time- frequency characteristic based partial discharge pulses identification technique for the high voltage XLPE power cables[J]. High Voltage Engineering, 2011, 37(8): 1997-2003.
[4] 司文荣, 李军浩, 袁鹏, 等. 基于波形非线性映射的多局部放电脉冲群快速分类[J]. 电工技术学报, 2009, 24(3): 216-221, 228. Si Wenrong, Li Junhao, Yuan Peng, et al. The fast grouping technique of PD sequence based on the nonlinear mapping of pulse shapes[J]. Transactions of China Electrotechnical society, 2009, 24(3): 216-221, 228.
[5] Sahoo N C, Salama M M A, Bartnikas R. Trends in partial discharge pattern classification: a survey[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2005, 12(2): 248-264.
[6] Mazroua A A, Salama M M A, Bartnikas R. PD pattern recognition with neural networks using the multilayer perceptron technique[J]. IEEE Transactions on Electrical Insulation, 1993, 28; 1082-1089.
[7] Cavallini A, Montanari G C, Contin A, et al. A new approach to the diagnosis of soild insulation systems based on PD signal inference[J]. IEEE Electrical Insulation Magazine, 2003, 19(2): 23-30.
[8] Lim Y, Chaotic Koo J. Analysis of partial discharge (CAPD) as a novel approach to investigate insulation degradation caused by the various defects[C]. Procee- dings of the IEEE International Symposium on Industrial Electronics, Pusan, Korea, 2001.
[9] Hao L, Lewin P L. Partial discharge source discri- mination using a support vector machine[J]. IEEE Transactions on Dielectric Electrical Insulation, 2010, 17(1): 189-197.
[10] 张晓星, 孙才新, 唐炬, 等. 基于统计不相关最优鉴别矢量集的GIS局部放电模式识别[J]. 电力系统自动化, 2006, 30(5): 59-62. Zhang Xiaoxing, Sun Caixin, Tang Ju, et al. PD pattern recognition based on optimal sets of statistical uncorrelated discriminant vectors in GIS[J]. Automation of Electric Power Systems, 2006, 30(5): 59-62.
[11] Lim Y, Koo J. Chaotic analysis of partial discharge (CAPD) -a novel approach to identify the nature of PD source[C]. Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 2001: 324-328.
[12] 郑升讯. 基于混沌特性的变压器局部放电特征提取及绝缘劣化的诊断[D]. 重庆: 重庆大学, 2010.
[13] 罗勇芬, 黄平, 李彦明. 油纸绝缘中局部放电时间序列的混沌特性及其模式识别[J]. 西安交通大学学报, 2010, 44(12): 55-60. Luo Yongfen, Huang Ping, Li Yanming. Chaotic characteristics of partial discharges time series in oil-paper insulation with applications to pattern recognition[J]. Journal of Xi’an Jiaotong University, 2010, 44(12): 55-60.
[14] Boeck W, et al. Diagnostic methods for GIS insulating systems[C]. CIGRE: 1992 Session, Paris: 151-182.
[15] 骆振华. 时间序列分析引论[M]. 厦门: 厦门大学出版社, 1987.
[16] Grassberger P, Procaccia I. Measuring the strangeness of strange attractors[J]. Physica D, 1983, 9(1/2): 189-208.
[17] Grebogi C, Ott E, Yorke J A. Final state sensitivity an obstruction to predictability[J]. Phys. Lett. A, 1983, 99: 415-419.
[18] Wolf A, Swift J B, Swinney H L, et al. Determining lyapunov exponents from time series[J]. Physica, 1985, D16: 285-317.
[19] 杨钟瑾. 核函数支持向量机[J]. 计算机工程与应用, 2008, 44(33): 1-6, 24. Yang Zhongjin. Kernel-based support vector machines [J]. Computer Engineering and Applications, 2008, 44(33): 1-6, 24.
[20] 姜磊, 朱德恒, 李福琪, 等. 基于人工神经网络的变压器绝缘模型放电模式识别的研究[J]. 中国电机工程学报, 2001, 21(1): 21-24. Jiang Lei, Zhu Deheng, Li Fuqi, et al. ANN based discharge pattern recognition of insulation models of electrical transformers[J]. Proceedings of the CSEE, 2001, 21(1): 21-24.