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dc.contributor.authorDahal, Keshav P.
dc.contributor.authorMcDonald, J.R.
dc.date.accessioned2009-03-04T16:50:51Z
dc.date.available2009-03-04T16:50:51Z
dc.date.issued1997
dc.identifier.citationDahal, K. P. and McDonald, J. R. (1997) A review of generator maintenance scheduling using artificial intelligence techniques. In: 32nd Universities Power Engineering Conference (UPEC `97), University of Manchester, September 10-12, 1997.en
dc.identifier.urihttp://hdl.handle.net/10454/2452
dc.description.abstractNew Artificial Intelligence (AI) approaches such as simulated annealing, genetic algorithms, simulated evolution, neural networks, tabu search, fuzzy logic and their hybrid techniques have been applied in recent years to solving Generator Maintenance Scheduling (GMS) problems. This paper presents a review of these AI approaches for the GMS problem. The formulation of problems and the methodologies of solution are discussed and analysed. A case study is also included which presents the application of a genetic algorithm to a test system based on a practical power system scenario.en
dc.language.isoenen
dc.rights© 1997 Universities Power Engineering Conference.en
dc.subjectMaintenance Schedulingen
dc.subjectPower generatorsen
dc.subjectGenetic algorithmsen
dc.subjectArtificial intelligenceen
dc.titleA review of generator maintenance scheduling using artificial intelligence techniquesen
dc.status.refereedYesen
dc.typeConference paperen
dc.type.versionAccepted Manuscripten
refterms.dateFOA2018-07-18T13:33:11Z


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