528 / 2022-03-28 16:45:35
Intelligent Diagnosis Method of GIS Mechanical Performance Based on VGG16
GIS,Mechanical Performance,Artificial Intelligence
终稿
Jipan Li / Heze Supply Company of State Grid Shandong Electric Power Company
Shoubin Yin / Heze Supply Company of State Grid Shandong Electric Power Company
Hongling Liu / Heze Supply Company of State Grid Shandong Electric Power Company
Shuofeng Niu / Heze Supply Company of State Grid Shandong Electric Power Company
Junjie Zhao / Heze Supply Company of State Grid Shandong Electric Power Company
Qiang Wang / North China Electric Power University
Mechanical fault is a common fault type in gas insulated switchgear (GIS). The mechanical performance of GIS is very important for the safe and stable operation of power system. In order to achieve accurate diagnosis of GIS mechanical fault, this paper proposes a feature fusion method based on VGG16 and multi-source signals. Firstly, fault simulation test was carried out on a real GIS prototype and feature signals were collected. Wavelet transform was used to obtain the wavelet scale coefficient map of feature signals and then the map was fused. Then adversarial generative network (WGAN) is used to expand the fused samples. Finally, VGG16 network is used to realize sample discrimination, and then complete GIS mechanical fault diagnosis. Experimental results show that the fault diagnosis accuracy of the proposed method is up to 95%, which is higher than that of traditional diagnosis methods, and the fusion samples have richer features than single signals. Meanwhile, the sample set expanded by data enhancement method can effectively solve the problem of insufficient generalization ability of deep learning classifier caused by the lack of samples.
重要日期
  • 会议日期

    09月25日

    2022

    09月29日

    2022

  • 08月15日 2022

    提前注册日期

  • 09月10日 2022

    报告提交截止日期

  • 11月10日 2022

    注册截止日期

  • 11月30日 2022

    初稿截稿日期

  • 11月30日 2022

    终稿截稿日期

主办单位
IEEE DEIS
承办单位
Chongqing University
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