Automating power system fault diagnosis through multi-agent system technology

S.D.J. McArthur, E.M. Davidson, J.A. Hossack, J.R. McDonald

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

66 Citations (Scopus)


Fault diagnosis within electrical power systems is a time consuming and complex task. SCADA systems, digital fault recorders, travelling wave fault locators and other monitoring devices are drawn upon to inform the engineers of incidents, problems and faults. Extensive research by the authors has led to the conclusion that there are two issues which must be overcome. Firstly, the data capture and analysis activity is unmanageable in terms of time. Secondly, the data volume leads to engineers being overloaded with data to interpret. This paper describes how multi-agent system technology, combined with intelligent systems, can be used to automate the fault diagnosis activity. Within the multi-agent system, knowledge-based and model-based reasoning are employed to automatically interpret SCADA system data and fault records. These techniques and the design of the multi-agent system architecture that integrates them are described. Consequently, the use of engineering assistant agents as a means of providing engineers with decision support, in terms of timely and summarised diagnostic information tailored to meet their personal requirements, is discussed.

Original languageEnglish
Title of host publicationSystem Sciences 2004
Subtitle of host publicationProceedings of the 37th Hawaii International Conference on System Sciences - 2004
Place of PublicationNew York
Number of pages8
ISBN (Print)0769520561
Publication statusPublished - 2004
Event37th Hawaii International Conference on System Sciences - 2004 - Big Island, Hawaii, United States
Duration: 3 Jan 20056 Jan 2005


Conference37th Hawaii International Conference on System Sciences - 2004
Country/TerritoryUnited States
CityBig Island, Hawaii


  • automating
  • power system fault diagnosis
  • multi-agent system technology
  • SCADA systems
  • data analysis
  • knowledge based systems
  • model-based reasoning


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