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Article: A statistics-based genetic algorithm for quality improvements of power supplies

TitleA statistics-based genetic algorithm for quality improvements of power supplies
Authors
KeywordsEvolutionary Algorithm
Orthogonal Array
Power Supplies
Power Systems
Issue Date2009
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ejie
Citation
European Journal Of Industrial Engineering, 2009, v. 3 n. 4, p. 468-492 How to Cite?
AbstractThis 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.
Persistent Identifierhttp://hdl.handle.net/10722/148607
ISSN
2015 Impact Factor: 0.718
2015 SCImago Journal Rankings: 0.987
References

 

DC FieldValueLanguage
dc.contributor.authorChan, KYen_US
dc.contributor.authorChan, KWen_US
dc.contributor.authorPong, GTYen_US
dc.contributor.authorAydin, MEen_US
dc.contributor.authorFogarty, TCen_US
dc.contributor.authorLing, SHen_US
dc.date.accessioned2012-05-29T06:14:04Z-
dc.date.available2012-05-29T06:14:04Z-
dc.date.issued2009en_US
dc.identifier.citationEuropean Journal Of Industrial Engineering, 2009, v. 3 n. 4, p. 468-492en_US
dc.identifier.issn1751-5254en_US
dc.identifier.urihttp://hdl.handle.net/10722/148607-
dc.description.abstractThis 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.en_US
dc.languageengen_US
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ejieen_US
dc.relation.ispartofEuropean Journal of Industrial Engineeringen_US
dc.subjectEvolutionary Algorithmen_US
dc.subjectOrthogonal Arrayen_US
dc.subjectPower Suppliesen_US
dc.subjectPower Systemsen_US
dc.titleA statistics-based genetic algorithm for quality improvements of power suppliesen_US
dc.typeArticleen_US
dc.identifier.emailChan, KY:kelvinc@pathology.hku.hken_US
dc.identifier.authorityChan, KY=rp00453en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1504/EJIE.2009.027038en_US
dc.identifier.scopuseid_2-s2.0-67651092028en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67651092028&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume3en_US
dc.identifier.issue4en_US
dc.identifier.spage468en_US
dc.identifier.epage492en_US
dc.publisher.placeUnited Kingdomen_US

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