K. Y. Chan
A statistics-based genetic algorithm for quality improvements of power supplies
Chan, K. Y.; Pong, Glory T.Y.; Aydin, Mehmet Emin; Fogarty, T. C.; Ling, S. H.
Authors
Glory T.Y. Pong
Dr Mehmet Aydin Mehmet.Aydin@uwe.ac.uk
Senior Lecturer in Networks and Mobile Computing
T. C. Fogarty
S. H. Ling
Abstract
This paper presents a new statistics-based evolutionary algorithm to improve the qualities of power supplies, in which operational costs and the stability of the power supply are optimised to provide a highly smooth but low-cost power supply service to customers. The proposed method is incorporated with the characteristics of the stochastic method, evolutionary algorithm and a more systematical statistical method, orthogonal design. It intends to compensate for the built-in randomness of the stochastic method and, at the same time, overcome the limitations of local search methods that are not suitable for handling multi-optima problems. Case studies on the WSCC 9-bus and New England 39-bus systems indicate that the proposed approach outperforms the existing method in terms of robustness in solution and convergence speed while the solution quality that can offer a more stable and cheaper power supply to customers is achieved. Copyright © 2009, Inderscience Publishers.
Citation
Chan, K. Y., Pong, G. T., Aydin, M. E., Fogarty, T. C., & Ling, S. H. (2009). A statistics-based genetic algorithm for quality improvements of power supplies. European Journal of Industrial Engineering, 3(4), 468-492. https://doi.org/10.1504/EJIE.2009.027038
Journal Article Type | Article |
---|---|
Publication Date | Jul 29, 2009 |
Journal | European Journal of Industrial Engineering |
Print ISSN | 1751-5254 |
Electronic ISSN | 1751-5262 |
Publisher | Inderscience |
Peer Reviewed | Peer Reviewed |
Volume | 3 |
Issue | 4 |
Pages | 468-492 |
DOI | https://doi.org/10.1504/EJIE.2009.027038 |
Keywords | power supply, power systems, evolutionary algorithm, orthogonal arrays, genetic algorithms, GAs, quality improvement, operational costs, stability, optimisation |
Public URL | https://uwe-repository.worktribe.com/output/1001257 |
Publisher URL | http://dx.doi.org/10.1504/EJIE.2009.027038 |
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