Feature Extraction and Pattern Recognition of Signals Radiated from Partial Discharge

被引：0
|
作者：
Liu Weidong [1 ]
Liu Shanghe [1 ]
Hu Xiaofeng [1 ]
机构：
[1] Mech Engn Coll, Electrostat & Electromagnet Protect Res Inst, Shijiazhuang, Hebei, Peoples R China
来源：
CEEM: 2009 5TH ASIA-PACIFIC CONFERENCE ON ENVIRONMENTAL ELECTROMAGNETICS | 2009年
关键词：
partial discharge; BP network; feature extraction; pattern recognition;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
The artificial neural networks based BP algorithm is used to recognize two typical discharge patterns, corona and spark. In order to have a comparison, feature extraction based on waveform parameter and time-frequency analysis were used separately to provide the training input. The results show that the highest average recognition rate based on waveform parameter reaches 92.5%, while this based on time-frequency is 95%. On the contrary, the lowest average recognition rate based on waveform parameter is 70%, while this based on time-frequency is 90%. This indicates that time-frequency analysis is more effective and more suitable for discharge pattern recognition.
引用
收藏
页码：114 / 117
页数：4
相关论文
共 50 条
  • [1] Feature extraction for multi source partial discharge pattern recognition
    Suresh, D
    INDICON 2005 PROCEEDINGS, 2005, : 309 - 312
  • [2] A New Hybrid Feature Extraction Method for Partial Discharge Signals Classification
    Liao, Ruijin
    Wang, Ke
    Yang, Lijun
    Duan, Lian
    Li, Jian
    PRZEGLAD ELEKTROTECHNICZNY, 2012, 88 (11A):
  • [3] Feature Extraction and Pattern Recognition Algorithm of Power Cable Partial Discharge Signal
    Du, Jie
    Mi, Jianwei
    Jia, Zhanpeng
    Mei, Jiaxiang
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2023, 37 (01)
  • [4] Partial Discharge Pattern Recognition Using Multi-scale Feature Extraction and Support Vector Machine
    Chan, Jeffery C.
    Ma, Hui
    Saha, Tapan K.
    2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES), 2013,
  • [5] The Feature Extraction and Pattern Recognition of Partial Discharge Type Using Fnergy Percentage of Wavelet Packet Coefficients and Support Vector Machines
    Xu, Jia
    Niu, Haiqing
    Hu, Riliang
    2015 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES (DRPT 2015), 2015, : 1776 - 1779
  • [6] Partial Discharge Pattern Recognition Based on Multiscale Features of Original Time Domain Signals
    Wang, Hao
    Liu, Yong
    Wang, Zhihui
    Han, T.
    Gao, Y.
    Du, B. X.
    2021 96TH IEEE CONFERENCE ON ELECTRICAL INSULATION AND DIELECTRIC PHENOMENA (CEIDP 2021) / 16TH IEEE NANOTECHNOLOGY MATERIALS AND DEVICES CONFERENCE (IEEE NMDC 2021), 2021, : 611 - 614
  • [7] Feature Optimization Selection and Dimension Reduction for Partial Discharge Pattern Recognition
    Wang, Shi-Qiang
    Zhang, Jia-Ning
    Hu, Hai-Yan
    Liu, Quan-Zhen
    Zhu, Ming-Xiao
    Mu, Hai-Bao
    Zhang, Guan-Jun
    2016 INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS (CMD), 2016, : 877 - 880
  • [8] Feature Extraction and Pattern Recognition of Vibration Signals in a Rolling Bearing
    He J.
    Yang S.
    Gan C.
    1600, Nanjing University of Aeronautics an Astronautics (37): : 1181 - 1186and1281
  • [9] GIS Partial Discharge Pattern Recognition Based on Multi-Feature Information Fusion of PRPD Image
    Yin, Kaiyang
    Wang, Yanhui
    Liu, Shihai
    Li, Pengfei
    Xue, Yaxu
    Li, Baozeng
    Dai, Kejie
    SYMMETRY-BASEL, 2022, 14 (11):
  • [10] Pattern Recognition for Partial Discharge Using Multi-Feature Combination Adaptive Boost Classification Model
    Yao, Rui
    Li, Jun
    Hui, Meng
    Bai, Lin
    Wu, Qisheng
    IEEE ACCESS, 2021, 9 : 48873 - 48883
12345