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局部放电脉冲群的实用快速分类技术及应用 被引量：21

Fast and Practical Grouping Technique for Partial Discharge Pulse Sequence and Its Application
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摘要 对108次/s高速采样率获取的超宽带局放脉冲波形-时间序列,使用等效时频法提取了局部放电脉冲群的波形特征参数,并研究了模糊C均值聚类算法的特性及其在局部放电脉冲波形特征向量参数处理中的应用.基于GIS的局部放电试验数据处理表明:由等效时频特征提取和模糊C均值聚类分析组成的局部放电脉冲群快速分类技术可以对多个局部放电源构成的脉冲群进行准确分类.这为研制基于单个脉冲的局部放电超宽带检测与模式识别系统提供了实用的脉冲群快速分类技术. A novel partial discharge （PD） detection and pattern recognition system are developed with ultra-wideband （UWB） based on a single pulse shape, where a fast grouping technique for PD pulse sequence is adopted. Equivalent time-frequency method （ETFM） is used to extract fea- tures for PD pulse shape-time sequence detected by UWB with a sampling rate of 10^8 per second. And the application of fuzzy C-means clustering （FCM） in fast grouping to PD pulse sequence is investigated based on feature parameters of pulse shapes. The analysis result of noised PD date of a gas insulated switch （GIS） shows that the fast grouping technique conducts a good separation for multi-PD pulses sequence which provides a feasible and practical fast grouping technique for PD pulse sequence to develop the multi-PDs detection and pattern recognition system.
作者 司文荣 李军浩 杨景刚 黎大健 梁永春 李彦明
机构地区 西安交通大学电气工程学院
出处 《西安交通大学学报》 EI CAS CSCD 北大核心 2008年第8期1021-1025,1060,共6页 Journal of Xi'an Jiaotong University
基金 河北省自然科学基金资助项目(F2007000636)
关键词 局部放电 脉冲波形-时间序列 等效时频 模糊C均值聚类 partial discharge pulse shape-time sequence equivalent time-frequency fuzzy C-means clustering
分类号 TM835.4 [电气工程—高电压与绝缘技术]
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参考文献9

  • 1CONTIN A, CAVALINI A, MONTANARI G C, et al. Digital detection and fuzzy classification of partial discharge signals[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2002, 9(2): 335-348.
  • 2CAVALINI A, MONTANARI G C, CONTIN A, et al. A new approach to the diagnosis of solid insulation systems based on PD signal inference[J]. IEEE Electrical Insulation Magazine, 2003, 19(2): 23-30.
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  • 4司文荣,李军浩,黎大健,杨景刚,李彦明.基于宽带检测的局放脉冲波形快速特征提取技术[J].电工电能新技术,2008,27(2):21-25. 被引量：44
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二级参考文献10

  • 1A Contin, A Cavallini, G C Montanari, et al. Digital detection and fuzzy classification of partial discharge signals [J]. IEEE Trans. on Dielectrics and Elec. Insulation, 2002, 9(2): 335-348
  • 2A Cavallini, G C Montanari, A Contin, et al. A new approach to the diagnosis of solid insulation systems based on PD signal inference [J]. IEEE Elec. Insulation Magazine, 2003, 19(2) : 23-30.
  • 3A CavaUini, G C Montanari, F Puletti. A fuzzy logic algorithm to detect electrical trees in polymeric insulation systems [ J ]. IEEE Trans. on Dielectrics and Elect. Insulation, 2005, 12(6) : 1134-1144.
  • 4M Caciari, A Contin, G C Montanari, et al. Identification and separation of two concurrent partial discharge phenomena [A]. IEEE Annual Report-Conference on Elec. Insulation and Dielectric Phenomena [C]. Millbrae, CA, USA: IEEE CS Press, 1996. 476-479.
  • 5A Contin, A Cavallini, G C Montanari, et al. Artificial intelligence methodology for separation and classification of partial discharge signals [ A]. Conference on Elec. Insulation and Dielectric Phenomena [ C ]. Victoria, BC, Canada: IEEE CS Press, 2000. 522-526.
  • 6A Cavallini, M Conti, G C Montanari. Indexes for the recognition of insulation system defects derived from partial discharge measurements [ A ]. Conference Record of the 2002 IEEE International Symposium on Elec. Insulation [ C ].Boston, MA, USA: IEEE CS Press, 2002. 511-515.
  • 7Pal NR, Bezdek JC. On clustering for the fuzzy c-means model [J]. IEEE Trans. FS, 1995, 3(3) : 370-379.
  • 8郑重,谈克雄,王猛,吴浩.基于脉冲波形时域特征的局部放电识别[J].电工电能新技术,2001,20(2):20-24. 被引量：37
  • 9王国利,郝艳捧,李彦明.电力变压器局部放电检测技术的现状和发展[J].电工电能新技术,2001,20(2):52-57. 被引量：131
  • 10郝艳捧,王国利,贾志东,蒋雄伟,谢恒.单个局部放电脉冲测量系统[J].电工电能新技术,2001,20(4):31-35. 被引量：9

共引文献43

同被引文献245

引证文献21

二级引证文献222

西安交通大学学报
2008年 第8期

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