Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-25T22:03:19.476Z Has data issue: false hasContentIssue false

Model/heuristic-based alarm processing for power systems*

Published online by Cambridge University Press:  27 February 2009

Monika Pfau-Wagenbauer
Affiliation:
Siemens Austria, Gudrunstrasse 11, A-1100 Vienna, Austria
Wolfgang Nejdl
Affiliation:
RWTH Aachen, Ahorn Str. 55, D-W-5100 Aachen, Germany

Abstract

This paper describes an intelligent alarm processing expert system which is integrated in a large Supervisory Control and Data Acquisition system for power distribution networks. The expert system works as an operator support tool by diagnosing network disturbances and device malfunctions. The expert system is based on a hierarchic, multi-level problem-solving architecture, integrating both model-based and heuristic techniques acting upon an object-oriented data structure. Several enhancements have been designed and implemented to enable the system to perform its task online and real-time. The expert system covers online processing of real-time data and intelligent alarm processing, as well as the automatic creation and update of the knowledge base. It consists of approximately 25000 objects (units) and 190 rules. The system uses the expert system tool KEE, runs on SUN workstations, and is integrated in the Supervisory Control and Data Acquisition system via LAN. The expert system was implemented for the Public Utilities Board Singapore controlling its 22 kV distribution network and has been online since November 1990.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bau, D. Y. and Brezillon, P. 1992. Model-based diagnosis of power station control systems: the SEPT experiment, IEEE Expert 7(1), pp. 3643.CrossRefGoogle Scholar
Bylander, T. and Chandrasekaran, B. 1985. Understanding behaviour using consolidation. Proceedings of the IJCAI '85 pp. 450454.Google Scholar
Chan, E. K. 1991. A realtime expert system integrated in a large SCADA system. Proceedings of the Third Symposium on Expert Systems Application to Power Systems, Tokyo, 04.Google Scholar
Chandrasekaran, B. and Mittal, B. 1983. Deep versus compiled knowledge approaches to diagnostic problem solving. International Journal of Man-Machine Studies 19(5), pp. 425436.CrossRefGoogle Scholar
Chandrasekaran, B., Smith, J. W. and Sticklen, J. 1989. Deep models and their relations to diagnosis. Artificial Intelligence in Medicine.CrossRefGoogle Scholar
Cigre, WG 38–06 on Expert Systems in Alarm Handling, 1991. Survey on expert systems in alarm handling. Prepared by Task Force 02 on Power System Monitoring and Alarm Processing.Google Scholar
de Kleer, J. and Williams, B. C. 1989. Diagnosis with behavioral modes. Proceedings of the 11th International Joint Conference on Artificial Intelligence pp. 13241330.Google Scholar
Friedrich, G., Gottlob, G. and Nejdl, W. 1990a. Generating efficient diagnostic procedures from model based knowledge using logic programming techniques. Computers and Mathematics with Applications, Special Issue on Logic Programming in Intelligent Decision and Control Systems. 20(9/10), pp. 5772.Google Scholar
Friedrich, G., Gottlob, G. and Nejdl, W. 1990b. Physical impossibility instead of fault models. Proceedings of the National Conference on Artificial Intelligence p. 331336.Google Scholar
Friedrich, G. and Nejdl, W. 1990. MOMO—Model-Based Diagnosis for Everybody. Proceedings of the 6th IEEE Conference on Artificial Intelligence Applications (CAIA), Santa Babara.Google Scholar
Hamscher, W. C. 1990. XDE: Diagnosing devices with hierarchic structure and known component failure modes. Proceedings of the 6th IEEE Conference on Artificial Intelligence Applications (CAIA), Santa Babara, pp. 4854.Google Scholar
Laffey, T. J. 1992. Real-time knowledge-based systems. Tutorial at the 8th Conference on Artificial Intelligence Applications (CAIA), Monterey.Google Scholar
Niebur, D. 1990. Expert systems for power system control in western Europe. Proceedings of the 5th IEEE International Symposium on Intelligent Control, Philadelphia.Google Scholar
Pfau-Wagenbauer, M. and Nejdl, W. 1992. Integrating model-based and heuristic features in a real-time expert system for power distribution networks. Proceedings of the 8th Conference on Artificial Intelligence Applications (CAIA), Monterey.Google Scholar
Pfau-Wagenbauer, M., Brunner, T. and Nejdl, W. 1992. Process Oriented Techniques in an Expert System for Power System Diagnosis. 10th European Conference on Artificial Intelligence (ECAI), Vienna.Google Scholar
Struss, P. and Dressier, O. 1989. Physical negation-integrating fault models into the general diagnostic engine. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 13181323, Detroit.Google Scholar