报告详情
Feature Analysis and Identification of Low-voltage Series Arc Fault
编号：336
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更新：2022-08-29 15:56:29 浏览：128次
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报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
Fires caused by arc faults are increasing every year, but low-voltage AC series arc faults are tricky to be detected by traditional methods because of its concealment. A detection algorithm based on multi-feature fusion to analyze arc faults is proposed in this paper. Furthermore, different loads, including motor loads and the power electronic loads, are selected to extract arc characteristics by combining various methods, especially Empirical Wavelet Transform (EWT). In contrast with some traditional modal decomposition algorithms, EWT can overcome modal aliasing and end-point effects, and improve the quality of the frequency band. Then, the change in signal complexity is calculated by introducing the Shannon entropy of the frequency band signal. In order to make the results more accurate, several classical features are integrated as the input elements of the neural network in this paper. Finally, the Scaled Conjugate Gradient Backpropagation algorithm is used to identify arc faults. The detection results with detection accuracy of over 99.8% by training the existing features, prove that the multi-feature fusion algorithm can detect AC arc faults effectively and accurately.
关键词
Energy Feature Extraction,waveform,time domain
报告人
Xiaoxue Chang
Nanjing University of Aeronautics and Astronautics稿件作者
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重要日期
-
会议日期
09月25日
2022
至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
联系方式
- Zhengyong Huang
- 61******@qq.com
历届会议
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2020年09月06日 中国 Beijing
2020 IEEE International Conference on High Voltage Engineering and Application -
2018年09月10日 希腊
2018 IEEE International Conference on High Voltage Engineering and Application -
2016年09月19日 中国 Chengdu, China
2016 IEEE International Conference on High Voltage Engineering and Application
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