Research on Partial Discharge Pattern Recognition in GIS Based on Mobilenet V2 and Improved WGAN
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摘要
Partial discharge (PD) is an important reason for the deterioration of GIS insulation performance. Accurate PD pattern recognition is of great significance to GIS operation and maintenance. Due to the small number of samples, low recognition accuracy and long running time of traditional PD pattern recognition methods, this paper proposed a GIS PD pattern recognition method based on improved WGAN and MobileNet-V2 network. Firstly, the test platform for PD was designed and built to obtain UHF signals under typical defects, and the PRPD spectrum of UHF signals was generated. Then, the improved WGAN was used to expand the PRPD spectrum. Finally, the pattern recognition of PD was realized based on MobileNet-V2 network. The results show that the proposed method which has less parameters can effectively solve the problem of insufficient data volume, and it has a high accuracy. So the model can be applied to the GIS operation and maintenance process, which has practical engineering value.
关键词
GIS,MobileNet,WGAN,Pattern Recognition,Partial Discharge
报告人
Shengfeng Lei
North China Electric Power University

稿件作者
Tao Li Heze Power Supply Company of Shandong Electric Power Company
Shuofeng Niu Heze Power Supply Company of Shandong Electric Power Company
Hongling Liu Heze Supply Company of State Grid Shandong Electric Power Company
Zhenzuo Li Heze Supply Company of State Grid Shandong Electric Power Company
Yinjing Du Heze Supply Company of State Grid Shandong Electric Power Company
Shengfeng Lei North China Electric Power University
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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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