DocumentCode :
1540803
Title :
Fast fault section estimation in distribution substations using matrix-based cause-effect networks
Author :
Chen, Wen-Hui ; Liu, Chih-Wen ; Tsai, Men-Shen
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Volume :
16
Issue :
4
fYear :
2001
fDate :
10/1/2001 12:00:00 AM
Firstpage :
522
Lastpage :
527
Abstract :
A new method for estimating fault sections in power substations is proposed. In this paper the knowledge representation and inference procedures based on the cause-effect networks (CE-Nets) and rule matrix transformation techniques are presented. The matrix based inference procedures are proposed to estimate the possible fault sections through simple matrix operations by transforming the established CE-Nets into matrix forms. The method is superior to existing production systems in the inference speed and the process of implementation. By testing on a typical Taiwan Power Company´s (Taipower) secondary substation, the experimental results show that the effectiveness of the proposed method even for the fault domains involving multiple faults and failure operations of protective devices. Moreover, it is easy to implement and transplant into different substations
Keywords :
fault diagnosis; inference mechanisms; knowledge representation; matrix algebra; power distribution faults; substations; Taiwan; cause-effect networks; distribution substations; failure operations; fast fault section estimation; fault domains; inference procedures; knowledge representation; matrix-based cause-effect networks; multiple fault; protective devices; rule matrix transformation; secondary substation; Artificial neural networks; Centralized control; Circuit faults; Control systems; Fault diagnosis; Intelligent networks; Knowledge representation; Power system faults; Power system reliability; Substations;
fLanguage :
English
Journal_Title :
Power Delivery, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8977
Type :
jour
DOI :
10.1109/61.956731
Filename :
956731
Link To Document :
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