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A review of generator maintenance scheduling using artificial intelligence techniques

Dahal, Keshav P.
; 
McDonald, J.R.
Publication Date
1997
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Keywords
Maintenance Scheduling, Power generators, Genetic algorithms, Artificial intelligence
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© 1997 Universities Power Engineering Conference.
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Yes
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openAccess
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Engineering and Digital Technology Publications
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1997-upec97-gms-1.pdf
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Abstract
New 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.
URI
http://hdl.handle.net/10454/2452
Version
Accepted manuscript
Citation
Dahal, 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.
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Conference paper
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