Online fault recognition of electric power cable in coal mine based on the minimum risk neural network

被引：0
|
作者：
汪梅 [1 ,2 ]
STATHAKI Tania [2 ]
机构：
[1] School of Electric and Control Engineering,Xi'an University of Science and Technology,Xi'an 710054,China
[2] Department of Electrical and Electronic Engineering,Imperial College,London SW7 2AZ,United Kingdom
来源：
International Journal of Coal Science & Technology | 2008年 / 14卷 / 03期
关键词：
minimum risk; neural network; traveling wave entropy; zero-order component; online cable; recognition algorithm; early fault;
D O I：
暂无
中图分类号：
TD61 [矿山输电与配电];
学科分类号：
0819 ;
摘要：
Firstly,the concepts of the traveling wave entropy and the feature function of traveling wave entropy were defined.Then the statistic characters of the traveling wave entropy feature function,mean value and variance were analyzed after the zero-order component of the traveling wave of online cable was selected to serve as the observed object.Finally,the new recognition algorithm of minimum risk neural network was pre- sented.The simulation experiments show that the recognitions of the early fault states can be completed correctly by using the proposed recognition algorithm.The classes of cable faults include in 1-phase ground faults,and the 2-phase short circuit faults or ground faults and the 3-phase short circuit faults or ground faults,open circuit.The fault resistance range is 1×10-1～1×109Ω.
引用
收藏
页码：492 / 496
页数：5
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