Browse > Article

Development of An Expert system with Knowledge Learning Capability for Service Restoration of Automated Distribution Substation  

Ko Yun-Seok (남서울대학 전자정보통신공학부)
Kang Tae-Gue (광운대 대학원 제어계측공학과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers A / v.53, no.12, 2004 , pp. 637-644 More about this Journal
Abstract
This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.
Keywords
Substation Automation; Bus Reconfiguration; Knowledge Learning; Pattern Recognition Expert System;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Luger, G.F. and Stubblefield, W.A. ARTIFICIAL INTELLIGENCE AND THE DESIGN OF EXPERT SYSTEM, the Beniman/Cummings Publishing Company, Inc.
2 C.S. Chang, T.S. Chung, 'An Expert System for On-Line Security-Economic Load Allocation on Distribution Systems,' IEEE Trans. on Power Delivery, Vol. 5, No. 1, pp. 467- 469, January 1990   DOI   ScienceOn
3 Aoki, K., H. Kuwabara, T. Satoh, and M. Kanez ashi, 'An Efficient Algorithm for Load Balancing of Transformers and Feeders by Switch Operation in Large Scale Distribution Systems,' IEEE Trans. on Power Delivery, Vol. 3, No. 4, pp. 1865-1872, October 1988   DOI   ScienceOn
4 고윤석, '대규모 SCADA 시스템을 위한 실시간 전문가시스템', 전기학회논문지, Vol. 48A, No. 6, pp. 781-788, 1999년 6월   과학기술학회마을
5 M. M. Adibi et al., 'Special Consideration in Power System Restoration The Second Working Group Report', IEEE Trans. onPWRS, Vol. 9, No.1, pp. 15-21, February 1994   DOI   ScienceOn
6 Yang, Hong-Tzer, Wen-Yeay Chang, Ching-Lien Huang, 'On-Line Fault Diagnosis of Power Substation Using Connectionist Expert System', IEEE Trans on Power Systems, Vol. 10, No. 1, February 1995   DOI   ScienceOn
7 Bernard J. P. and D. Durocher, 'An Expert System for Fault Diagnosis Integrated in Existing SCADA Systems,' IEEE Trans. on Power Systems, Vol. 9, No. 1, pp. 548-554, February 1994   DOI   ScienceOn
8 Kim, H., Y. Ko, and K. H. Jung, 'Algorithm of Trans ferring the Load of the Faulted Substation Transformer using the Best-First Search Method,' IEEE Trans. on PWRD, Vol. 7, No. 3, pp. 1434-1442, July 1992   DOI   ScienceOn
9 Taylor T. and D. Lubkeman, 'Implementation of Heur istic Search Strategies for Distribution Feeder Reconfiguration,' IEEE Trans. on Power Delivery, Vol. 5, No. 2, pp. 239-246, January 1990   DOI   ScienceOn
10 Mori S., I. Hata, T. Usui and K. Morita,'Expert System Supporting Substation Service Restoration,' ESAP IV, pp. 131-136, January 1993