Faulty line selection based on waveform feature clustering in time domain for resonance grounding system

被引:0
|
作者
Guo, Moufa [1 ]
Yan, Min [2 ]
Chen, Bin [3 ]
Yang, Gengjie [1 ]
机构
[1] College of Electrical Engineering and Automation, Fuzhou University, Fuzhou,350116, China
[2] Fuzhou Power Supply Company of State Grid Fujian Electric Power Company, Fuzhou,350003, China
[3] Electric Power Research Institute of State Grid Fujian Electric Power Company, Fuzhou,350007, China
基金
中国国家自然科学基金;
关键词
Electric grounding - Power quality - Entropy - Feature extraction - Graphic methods - Time domain analysis - Transients;
D O I
10.16081/j.issn.1006-6047.2015.11.010
中图分类号
学科分类号
摘要
The waveform similarity of line zero-sequence currents after the single-phase grounding fault of resonance grounding system is analyzed based on its zero-sequence network and a method of faulty line selection based on the waveform feature clustering in the time domain is proposed for it. The line zero-sequence current waveform of the first post-fault half-period is decomposed in histograms and the relative entropy matrix is adopted to reflect the state difference among the zero-sequence currents, as well as their polarity information. Combined with the amplitude information, an integrated relative entropy matrix is established to represent the time-domain waveform feature of transient zero-sequence currents. The fuzzy KFCM (Kernel Fuzzy C-Means) without the threshold setting is used to detect the faulty line. The proposed method is verified by the simulations under different working conditions and the results show that, it is immune to the arc fault, noise disturbance, non-synchronized sampling and compensation degree. © 2015, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码:59 / 66
相关论文
共 50 条
  • [41] Curious Feature Selection-Based Clustering
    Moran M.
    Gordon G.
    IEEE Transactions on Artificial Intelligence, 2024, 5 (12): : 1 - 13
  • [42] Text Categorization Based on Clustering Feature Selection
    Zhou, Xiaofei
    Hu, Yue
    Guo, Li
    2ND INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT, ITQM 2014, 2014, 31 : 398 - 405
  • [43] Feature Selection for Local Learning Based Clustering
    Zeng, Hong
    Cheung, Yiu-ming
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2009, 5476 : 414 - 425
  • [44] LDA Based Feature Selection for Document Clustering
    Kumar, B. Shravan
    Ravi, Vadlamani
    COMPUTE'17: PROCEEDINGS OF THE 10TH ANNUAL ACM INDIA COMPUTE CONFERENCE, 2017, : 125 - 130
  • [45] A fuzzy clustering based algorithm for feature selection
    Sun, HJ
    Wang, SR
    Mei, Z
    2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 1993 - 1998
  • [46] Feature Selection for Density-Based Clustering
    Ling, Yun
    Ye, Chongyi
    2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION, 2009, : 226 - 229
  • [47] Adaptive clustering and feature selection for categorical time series using interpretable frequency-domain features
    Bruce, Scott A.
    STATISTICS AND ITS INTERFACE, 2023, 16 (02) : 319 - 335
  • [48] Character line segmentation based on feature clustering
    Xi, Yan
    Chen, Youbin
    Liao, Qingmin
    Leung Winghong
    Fung Shunming
    Deng Jiangwen
    ICDAR 2007: NINTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS, 2007, : 402 - +
  • [49] Faulty feeder detection based on the composite factors in resonant grounding distribution system
    Tang, Tao
    Zeng, Xiangjun
    Huang, Chun
    Li, Zewen
    ELECTRIC POWER SYSTEMS RESEARCH, 2020, 189
  • [50] Faulty line selection based on time-frequency characteristics of transient zero-sequence current
    Shu, Hongchun
    Zhu, Mengmeng
    Huang, Wenzhen
    Duan, Ruimin
    Dong, Jun
    Gao, Li
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2013, 33 (09): : 1 - 6