高电压技术2008，Vol.34Issue(12)：2713-2718,6.
基于粗糙集理论的配电网故障诊断规则提取方法
Rough Set Theory Based Approach for Fault Diagnosis Rule Extraction of Distribution System
摘要
Abstract
As the first step of service restoration of distribution system, rapid fault diagnosis is a significant task for reducing power outage time, decreasing outage loss, and subsequently improving service reliability and safety. This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task. Aiming at this reduction problem, a heuristic reduction algorithm based on attribution length and frequency is proposed. At the same time, the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction. Meanwhile, a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking. Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise. Moreover, it needs less calculation towards specific discernibility matrix, and thus avoids the corresponding NP hard problem. The whole process is realized by MATLAB programming. A simulation example shows that the method has a fast calculation speed, and the extracted rules can reflect the characteristic of fault with a concise form. The rule database, formed by different reduction of decision table, can diagnose single fault and multi-faults efficiently, and give satisfied results even when the existed information is incomplete. The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.关键词
fault diagnosis/distribution system/reduction algorithm/rule extraction/rule matching/rough set theoryKey words
fault diagnosis/distribution system/reduction algorithm/rule extraction/rule matching/rough set theory分类
动力与电气工程引用本文复制引用
周永勇,周湶,刘佳宾..基于粗糙集理论的配电网故障诊断规则提取方法[J].高电压技术,2008,34(12):2713-2718,6.基金项目
National Natural Science Foundation of China (50607023), Natural Science Foundation of CQ CSTC(2006BB2189) （50607023）
