Application of WE-AE-BP Method to Electric Shock Faults Identification in the Low-voltage Distribution Network
编号:99 访问权限:公开 更新:2020-10-26 14:57:34 浏览:243次 张贴报告

报告开始:2020年11月04日 15:50(Asia/Shanghai)

报告时间:5min

所在会场:[G] Poster session [G1] Poster Session 1 and Poster Session 6

摘要
In the low-voltage distribution network, the tripping criterion of the residual current devices is usually a fixed threshold. The incorrect detection of leakage current signal may lead to tripping delay or mis-trip of the devices in electric shock accidents, which may be caused by the equipment vibration, harmonics, transient disturbances, etc. Hence, this study proposes an artificial intelligence method for quickly identifying electric shock faults to improve the function of the device. Firstly, wavelet entropy removes noise from the electric shock signal. Then, the autoencoder is applied to extract the total leakage current waveform as the feature information. Finally, the back-propagation neural network classifies the electric shock types. The simulation and experiment platforms are established to obtain experimental samples and validate the reliability of the proposed method. Compared to other alternative methods, the proposed method shows outstanding identification ability, which demonstrates its applicability in reality.
关键词
Autoencoder, back-propagation, electric shock faults identification, low-voltage distribution network,wavelet entropy denoising
报告人
Wu Shuang
Fuzhou University

稿件作者
Wu Shuang Fuzhou University
Lin Shu-Yue Fuzhou University
Guo Mou-Fa Fuzhou University
发表评论
验证码 看不清楚,更换一张
全部评论
重要日期
  • 会议日期

    11月02日

    2020

    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
联系方式
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询