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Conference Paper: A novel ANN fault diagnosis system for power systems using dual GA loops in ANN training

TitleA novel ANN fault diagnosis system for power systems using dual GA loops in ANN training
Authors
KeywordsArtificial neural network (ANN)
Fault diagnosis
Genetic algorithm (GA)
Power system
Issue Date2000
PublisherIEEE.
Citation
IEEE Power Engineering Society Summer Meeting, Seattle, WA, 16-20 July 2000, v. 1, p. 425-430 How to Cite?
AbstractFault diagnosis is of great importance to the rapid restoration of power systems. Many techniques have been employed to solve this problem. In this paper, a novel Genetic Algorithm (GA) based neural network for fault diagnosis in power systems is suggested, which adopts three-layer feed-forward neural network. Dual GA loops are applied in order to optimize the neural network topology and the connection weights. The first GA-loop is for structure optimization and the second one for connection weight optimization. Jointly they search the global optimal neural network solution for fault diagnosis. The formulation and the corresponding computer flow chart are presented in detail in the paper. Computer test results in a test power system indicate that the proposed GA-based neural network fault diagnosis system works well and is superior as compared with the conventional Back-Propagation (BP) neural network.
Persistent Identifierhttp://hdl.handle.net/10722/46339
References

 

DC FieldValueLanguage
dc.contributor.authorBi, TSen_HK
dc.contributor.authorNi, YXen_HK
dc.contributor.authorShen, CMen_HK
dc.contributor.authorWu, FFen_HK
dc.date.accessioned2007-10-30T06:47:42Z-
dc.date.available2007-10-30T06:47:42Z-
dc.date.issued2000en_HK
dc.identifier.citationIEEE Power Engineering Society Summer Meeting, Seattle, WA, 16-20 July 2000, v. 1, p. 425-430en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46339-
dc.description.abstractFault diagnosis is of great importance to the rapid restoration of power systems. Many techniques have been employed to solve this problem. In this paper, a novel Genetic Algorithm (GA) based neural network for fault diagnosis in power systems is suggested, which adopts three-layer feed-forward neural network. Dual GA loops are applied in order to optimize the neural network topology and the connection weights. The first GA-loop is for structure optimization and the second one for connection weight optimization. Jointly they search the global optimal neural network solution for fault diagnosis. The formulation and the corresponding computer flow chart are presented in detail in the paper. Computer test results in a test power system indicate that the proposed GA-based neural network fault diagnosis system works well and is superior as compared with the conventional Back-Propagation (BP) neural network.en_HK
dc.format.extent594516 bytes-
dc.format.extent2950 bytes-
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dc.format.extent11910 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
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dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings of the IEEE Power Engineering Society Transmission and Distribution Conferenceen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2000 IEEE. 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 to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectArtificial neural network (ANN)en_HK
dc.subjectFault diagnosisen_HK
dc.subjectGenetic algorithm (GA)en_HK
dc.subjectPower systemen_HK
dc.titleA novel ANN fault diagnosis system for power systems using dual GA loops in ANN trainingen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailNi, YX: yxni@eee.hku.hken_HK
dc.identifier.emailWu, FF: ffwu@eee.hku.hken_HK
dc.identifier.authorityNi, YX=rp00161en_HK
dc.identifier.authorityWu, FF=rp00194en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/PESS.2000.867624en_HK
dc.identifier.scopuseid_2-s2.0-0038493561en_HK
dc.identifier.hkuros73301-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0038493561&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1en_HK
dc.identifier.spage425en_HK
dc.identifier.epage430en_HK
dc.identifier.scopusauthoridBi, TS=6602683764en_HK
dc.identifier.scopusauthoridNi, YX=7402910021en_HK
dc.identifier.scopusauthoridShen, CM=7402860197en_HK
dc.identifier.scopusauthoridWu, FF=7403465107en_HK

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