Research on Recognition Method of Living Body Shock in Low-voltage Distribution Network Based on BP Neural Network

被引：0
|
作者：
Cai Z. [1 ]
Guo M. [1 ]
Wei Z. [1 ]
机构：
[1] College of Electrical Engineering and Automation, Fuzhou University, Fuzhou
来源：
Dianwang Jishu/Power System Technology | 2022年 / 46卷 / 04期
基金：
中国国家自然科学基金;
关键词：
BP neural network; Living body electrocution; Low-voltage distribution network; Mallat algorithm; Type of electric shock recognition;
D O I：
10.13335/j.1000-3673.pst.2021.0742
中图分类号：
学科分类号：
摘要：
Existing residual current protectors mostly use the effective value of the total residual current as the operating criterion. The threshold is fixed, and cannot identify the type of electric shock. A low-voltage distribution network living body electric shock identification method based on adaptive threshold and BP neural network is proposed. The total residual current signal is processed by Mallat algorithm to reduce noise, and an adaptive threshold is constructed from the obtained low-frequency components, which is used to determine the time of electric shock, extract statistical features that can characterize the characteristics of living bodies and the BP neural network is trained to establish an electric shock type recognition model. The physical simulation results show that the method can meet the requirements of rapidity and reliability of the residual current protector, and the accuracy rate of electric shock type identification is 99.93%, which has reference value for the development of a new generation of residual current protection device. © 2022, Power System Technology Press. All right reserved.
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页码：1614 / 1623
页数：9
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