[1]段培永,窦甜华,杨修文,等.基于CMAC神经网络的低压故障电弧检测[J].山东建筑大学学报,2011,(02):105-109.
 DUAN Pei-yong,DOU Tian-hua,YANG Xiu-wen,et al.Fault arcing detection for building low voltage lines based on CMAC neural network[J].Journal of Shandong jianzhu university,2011,(02):105-109.
点击复制

基于CMAC神经网络的低压故障电弧检测()
分享到:

《山东建筑大学学报》[ISSN:1673-7644/CN:37-1449/TU]

卷:
期数:
2011年02期
页码:
105-109
栏目:
研究论文
出版日期:
2011-04-15

文章信息/Info

Title:
Fault arcing detection for building low voltage lines based on CMAC neural network
作者:
段培永窦甜华杨修文郭东东邹苒
山东建筑大学 信息与电气工程学院 ,山东 济南 250101
Author(s):
DUAN Pei-yong DOU Tian-hua YANG Xiu-wen et al.
School of Information & Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
关键词:
CMAC神经网络故障电弧检测信息融合
Keywords:
CMAC neural network arcing fault detection information fusion
分类号:
TP183
文献标志码:
A
摘要:

低压供配电线路中的故障电弧由于其电流值小,不足以使传统断路器动作,且电路中存在与故障电弧波形特征相似的负载,使故障电弧成为产生电气火灾的主要原因之一。采用单一判据判断故障

电弧,误判率较高。本文通过搭建实验平台,有效模拟建筑物低压供配电线路中的故障电弧,分析故障电弧特征,提取出表征故障电弧的特征量。使用CMAC神经网络建立模型,将各周期采样点均值的差

值和小波高频系数两种判据融合,克服单一判据的不确定性和局限性,所提出的信息融合方法可有效提高辨识故障电弧的准确率。

Abstract:

The value of arcing fault current is too small to make the traditional circuit breaker to cut off the power supply, and there are loads which have the similar

characters to arcing fault in the circuit, so arcing fault is one of the major causes of electrical fire. Simplex criterion used to detect fault arcing has the shortcoming of

high miscarriage rate. In this paper, the fault arcing of building low voltage distribution lines is simulated through building an experiment platform and the characteristics of

fault arcing are extracted. CMAC neural network is used to build a model to fuse two criteria which are the differences of the mean values of sample points per cycle and the

wavelet high frequency coefficient to overcome the uncertainty and limitation of simplex criterion, and the presented method of the multi-information fusion can improve the

accuracy of identifying fault arcing effectively.

相似文献/References:

[1]段培永,徐丽平,石嘉川,等.基于小波系数均差值的低压电弧故障诊断方法[J].山东建筑大学学报,2014,(01):1.
 Duan Peiyong,Xu Liping,Shi Jiachuan,et al.Arc fault diagnosis method for low voltage lines based on the mean and difference of wavelet coefficients[J].Journal of Shandong jianzhu university,2014,(02):1.

更新日期/Last Update: 2011-07-05