291 / 2021-11-09 10:37:00
Acoustic Detection and Decision Fusion Recognition of PD in Power Cable
partial discharge,acoustic,XLPE power cable,decision fusion
终稿
Yun Wang / State Key Laboratory of Power Transmission Equipment & System Security and New Technology; Chongqing University
Lan Xiong / State Key Laboratory of Power Transmission Equipment & System Security and New Technology;Chongqing University
Hailong Tang / State Key Laboratory of Power Transmission Equipment & System Security and New Technology;Chongqing University
Zhanlong Zhang / Chongqing University; State Key Laboratory of Power Transmission Equipment & System Security and New Technology
Acoustic detection has features of non-contact monitoring, no influence on the operation of tested equipment, and little interference by electromagnetic noise. In this paper, the acoustic method is applied to detect the partial discharge (PD) in power cable. Meanwhile, the accuracy of recognition is improved by signal processing, multi-feature construction, and algorithm optimization. The short-term energy and centroid frequency of power spectral density is extracted and the acoustic PRPD is constructed. A decision algorithm based on the fusion of improved K-Nearest Neighbor (KNN) and Back Propagation Neural Network (BPNN) is proposed. Cables with three types of defects were manually processed and acoustic PD samples were acquired. The results show that the PD can be detected quickly by KNN, and acoustic PRPD features used by BPNN showed a high recognition rate. The reliability of PD recognition is effectively improved by combining with the results of KNN and BPNN.
重要日期
  • 会议日期

    07月11日

    2023

    08月18日

    2023

  • 11月10日 2021

    初稿截稿日期

  • 12月10日 2021

    注册截止日期

  • 12月11日 2021

    报告提交截止日期

主办单位
IEEE IAS
承办单位
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
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