特征图谱时频信号放电识别算法研究与现场应用

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Abstract High-voltage electrical equipment, the local discharge of the field detection process, usually mixed with a variety of interference signals, which will be part of the discharge fault analysis and judgment difficult. The effective suppression of the interference signal is a prerequisite for partial discharge mode identification and fault judgment. Based on the time-frequency distribution characteristics of partial discharge signals, the equivalent duration and the equivalent frequency bandwidth are used to characterize the time-frequency characteristics of partial discharge wideband pulse waveforms, and the equivalent time-frequency spectrum is constructed. The partial discharge signals of high voltage switchgear are classified by feature map classification method. Finally, the validity of this method is verified by experiments.

Key words： partial discharge feature extraction characteristic map

Received: 14 October 2019

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	Articles by authors
	Zhou Xiong
	Zhou Zemin
	Peng Yanjun
	Teng Benke
	Tang Ming

Cite this article:

Zhou Xiong,Zhou Zemin,Peng Yanjun等. Research and field application on discharge identification algorithm of characteristic spectrum time-frequency signal[J]. Electrical Engineering, 2020, 21(6): 63-68.

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http://dqjs.cesmedia.cn/EN/Y2020/V21/I6/63

[1] Cavallini A, Contin A, Montanari G C, et al.Advanced PD inference in on-field measurements. I. Noise rejection[J]. IEEE Trans. Dielectr. Electr. Insul., 2003, 10: 216-224.
[2] Partial discharge detection in medium voltage and high voltage cables: maximum distance for detection, length of cable,some answers[J]. IEEE Electrical Insulation Magazine, 2016, 32(5): 41-46.
[3] Cavallini A, Montanari G C, Puletti F.A novel method to locate PD in polymeric cable systems based on amplitude-frequency (AF) Map[J]. IEEE Trans. Dielectr. Electr. Insul., 2007, 14(3): 726-734.
[4] 于洪乾, 闫祥申, 祝世辉, 等. 局部放电检测在开关柜中的应用[J]. 电气技术, 2015, 16(6): 123-125.
[5] 祝丽业. 局部放电信号干扰抑制与识别方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2013.
[6] 卞超, 陈少波. 基于脉冲电流波形的GIS局部放电检测抗干扰技术研究[J]. 高压电器, 2010, 46(5): 30-33.
[7] 何平. 变压器内部放电故障诊断系统的设计与研究[J]. 电气技术, 2016, 17(4): 97-100.
[8] 王振远, 谈克雄, 朱德恒, 等. 根据脉冲波形特征识别几种典型模型放电的研究[J]. 电工技术学报, 1997, 12(6): 35-38.
[9] 王彩雄, 唐志国, 常文治, 等. 一种多源局部放电信号分离方法[J]. 中国电机工程学报, 2013, 33(13): 212-219, 前插26.
[10] 鲍永胜. 局部放电脉冲波形特征提取及分类技术[J]. 中国电机工程学报, 2013, 33(28): 168-175.
[11] 葛哲学, 陈仲生. Matlab时频分析技术及其应用[M]. 北京: 人民邮电出版社, 2006.
[12] 王猛, 谈克雄, 高文胜, 等. 局部放电脉冲波形的时频联合分析特征提取方法[J]. 电工技术学报, 2002, 17(2): 76-79.
[13] 邱鹏锋, 郑连清, 魏成伟. 一种GIS局部放电模式识别新方法及应用[J]. 电气技术, 2017, 18(8): 12-16.
[14] 陈敬德, 李峰, 孙源文, 等. 基于KNN和MSR的局部放电模式识别研究[J]. 电气技术, 2018, 19(1): 10-14.
[15] 李端姣, 朱文俊, 王红斌, 等. 变电站全站局部放电检测中多源信号分离方法[J]. 南方电网技术, 2013, 7(2): 91-95.
[16] 夏云峰, 高胜友, 陈锡阳, 等. 波形特征与统计特征相结合的局部放电脉冲分类方法[J]. 高压电器, 2015, 51(8): 146-151.
[17] 鲍永胜, 郝峰杰, 徐建忠, 等. GIS局部放电脉冲分类特征提取算法[J]. 电工技术学报, 2016, 31(9): 181-188.
[18] 陈贤熙. 特征图谱分类法在避雷器局部放电测试分析中的应用[J]. 绝缘材料, 2015, 48(9): 65-69.
[19] 司文荣, 李军浩, 杨景刚, 等. 局部放电脉冲群的实用快速分类技术及应用[J]. 西安交通大学学报, 2008, 42(8): 1021-1025, 1060.
[20] 刘耀年, 伏祥运, 张文生, 等. 基于模糊识别与模糊聚类理论的短期负荷预测[J]. 电工技术学报, 2002, 17(5): 83-86, 95.