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Artificial intelligence recognition method of living body electric shock in low voltage distribution networks
编号：98 访问权限：仅限参会人 更新：2020-10-15 18:43:09 浏览：448次 口头报告

报告开始：2020年11月02日 17:00（Asia/Shanghai）

报告时间：15min

所在会场：[A] Power System [A1] Session 1 and Session 6

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摘要
  In the low-voltage distribution network, it is difficult to determine the moment of electric shock and distinguish the types of electric shock by detecting the total leakage current. In order to solve the problems, a recognition method of electric shock based on artificial intelligence is proposed in this paper. The constructed adaptive threshold is used to detect the mutation amount of the total leakage current, which achieves the purpose of determining the moment of electric shock. And then, according to the different waveform characteristics of living and non-living bodies after electric shock, the electric shock accidents are classified. The results show that the proposed method can effectively detect electric shock signals and has reference value for the development of a new generation of residual current protectors.
关键词
Classification of electric shock accidents, Electric shock signal detection, Low-voltage distribution network, Neural networks
报告人
Wei Zheng-feng
Fuzhou University

稿件作者
Wei Zheng-feng Fuzhou University
Guo Mou-fa Fuzhou University
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重要日期

  • 会议日期

    11月02日

    2020

    至

    11月04日

    2020

  • 10月27日 2020

    初稿截稿日期

  • 11月03日 2020

    报告提交截止日期

  • 11月04日 2020

    注册截止日期

  • 11月17日 2020

    终稿截稿日期

主办单位
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
承办单位
Huazhong University of Science and Technology
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