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基于小波分析和BP神经网络的触电信号检测模型
引用本文:李春兰,苏 娟,杜松怀,夏 越,张俊杰,张力淼.基于小波分析和BP神经网络的触电信号检测模型[J].农业工程学报,2010,26(14):130-134.
作者姓名:李春兰  苏 娟  杜松怀  夏 越  张俊杰  张力淼
作者单位:1. 中国农业大学信息与电气工程学院,北京 100083;2. 新疆农业大学机械交通学院,乌鲁木齐 830052;1. 中国农业大学信息与电气工程学院,北京 100083;1. 中国农业大学信息与电气工程学院,北京 100083;1. 中国农业大学信息与电气工程学院,北京 100083;3. 河南省平顶山供电局,平顶山 467000;3. 河南省平顶山供电局,平顶山 467000
基金项目:国家“十一五”科技支撑项目(2006BAJ04B06);中央高校基本科研业务费专项课题(2009JS101);河南省电力科学技术基金(201001农-2)
摘    要:针对从农村低压电网总泄漏电流中检测和判断触电电流信号的难题,该文提出一种基于小波变换和BP神经网络的触电信号检测方法。首先用触电物理实验平台对动物触电信号进行实测,选择合适的小波基和分解尺度对触电实验中总泄漏电流及触电电流进行小波多分辨分析,实现原始信号的预处理;再将预处理后的波形作为样本进行神经网络学习和训练,建立从总泄漏电流波形中提取触电电流波形的神经网络耦合模型,并用此模型对未训练的样本进行触电信号的检测,检测值与实际值的平均相对误差为3.93%,说明该方法能够从总泄漏电流中检测出触电电流信号,对于

关 键 词:小波分析,神经网络,信号检测,剩余电流保护装置
收稿时间:9/5/2010 12:00:00 AM
修稿时间:2010/10/13 0:00:00

Detecting model of electric shock signal based on wavelet analysis and BP neural network
Li Chunlan,Su Juan,Du Songhuai,Xia Yue,Zhang Junjie and Zhang Limiao.Detecting model of electric shock signal based on wavelet analysis and BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(14):130-134.
Authors:Li Chunlan  Su Juan  Du Songhuai  Xia Yue  Zhang Junjie and Zhang Limiao
Abstract:It is difficult to exactly detect and judge a weak electric shock signals in the summation leakage current on the low-voltage electric power grid. Integrating the merit of wavelet transform with that of BP neural network, a novel detection of the electric shock current based on wavelet transform and BP neural network was introduced in this paper. First of all, the animal electric shock signals was tested by physical experiment of electric shock, and an appropriate wavelet base and decompose scale was chosen to analysis the summation leakage current and the electric shock current. And then, the wave shape of specimens pretreated by wavelet transform was trained by BP neural network. A neural network coupling model of extracting electric shock current from the summation leakage current was built, and used to detect electric shock current of the untrained specimens. The average relative error between detected value and actual value was 3.93%. The result indicated that this method can detect electric shock current in the summation leakage current, and can be a reference for the development of new generation residual current operated devices.
Keywords:wavelet analysis  neural network  signal detection  residual current operated device
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