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GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

Dahal, Keshav P.
; 
Burt, G.M.
; 
McDonald, J.R.
; 
Galloway, S.J.
Publication Date
2000
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Keywords
Genetic Algorithms, Simulated Annealing, Generator Maintenance, Scheduling
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© 2000 IEEE. Reprinted from Proceedings of the Congress on Evolutionary Computation - CEC. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bradford's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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Abstract
Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems
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http://hdl.handle.net/10454/952
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Citation
Dahal, K.P., Burt, G.M., McDonald, J.R. and Galloway, S.J. (2000). GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems. Congress on Evolutionary Computation (CEC). La Jolla, CA, USA. 16-19 July 2000. Proceedings of the Congress on Evolutionary Computation. Vol. 1., pp. 567-574.
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https://doi.org/10.1109/CEC.2000.870347
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Conference paper
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