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Article: An on-line distributed intelligent fault section estimation system for large-scale power networks

TitleAn on-line distributed intelligent fault section estimation system for large-scale power networks
Authors
KeywordsDistributed intelligent system
Fault section estimation
Graph partitioning
Large-scale power networks
Issue Date2002
PublisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/epsr
Citation
Electric Power Systems Research, 2002, v. 62 n. 3, p. 173-182 How to Cite?
AbstractIn this paper, a novel distributed intelligent system is suggested for on-line fault section estimation (FSE) of large-scale power networks. As the first step, a multi-way graph partitioning method based on weighted minimum degree reordering is proposed for effectively partitioning the original large-scale power network into desired number of connected sub-networks with quasi-balanced FSE burdens and minimum frontier elements. After partitioning, a distributed intelligent system based on Radial Basis Function Neural Network (RBF NN) and companion fuzzy system is suggested for FSE. The relevant theoretical analysis and procedure are presented in the paper. The proposed distributed intelligent FSE method has been implemented with sparse storage technique and tested on the IEEE 14, 30 and 118-bus systems, respectively. Computer simulation results show that the proposed FSE method works successfully for large-scale power networks. © 2002 Elsevier Science B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73820
ISSN
2015 Impact Factor: 1.809
2015 SCImago Journal Rankings: 1.256
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorBi, Ten_HK
dc.contributor.authorNi, Yen_HK
dc.contributor.authorShen, CMen_HK
dc.contributor.authorWu, FFen_HK
dc.date.accessioned2010-09-06T06:55:04Z-
dc.date.available2010-09-06T06:55:04Z-
dc.date.issued2002en_HK
dc.identifier.citationElectric Power Systems Research, 2002, v. 62 n. 3, p. 173-182en_HK
dc.identifier.issn0378-7796en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73820-
dc.description.abstractIn this paper, a novel distributed intelligent system is suggested for on-line fault section estimation (FSE) of large-scale power networks. As the first step, a multi-way graph partitioning method based on weighted minimum degree reordering is proposed for effectively partitioning the original large-scale power network into desired number of connected sub-networks with quasi-balanced FSE burdens and minimum frontier elements. After partitioning, a distributed intelligent system based on Radial Basis Function Neural Network (RBF NN) and companion fuzzy system is suggested for FSE. The relevant theoretical analysis and procedure are presented in the paper. The proposed distributed intelligent FSE method has been implemented with sparse storage technique and tested on the IEEE 14, 30 and 118-bus systems, respectively. Computer simulation results show that the proposed FSE method works successfully for large-scale power networks. © 2002 Elsevier Science B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/epsren_HK
dc.relation.ispartofElectric Power Systems Researchen_HK
dc.subjectDistributed intelligent systemen_HK
dc.subjectFault section estimationen_HK
dc.subjectGraph partitioningen_HK
dc.subjectLarge-scale power networksen_HK
dc.titleAn on-line distributed intelligent fault section estimation system for large-scale power networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0378-7796&volume=62&spage=172&epage=182&date=2002&atitle=An+on-line+distributed+intelligent+fault+section+estimation+system+for+large-scale+power+networksen_HK
dc.identifier.emailNi, Y: yxni@eee.hku.hken_HK
dc.identifier.emailWu, FF: ffwu@eee.hku.hken_HK
dc.identifier.authorityNi, Y=rp00161en_HK
dc.identifier.authorityWu, FF=rp00194en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0378-7796(02)00042-1en_HK
dc.identifier.scopuseid_2-s2.0-0037189712en_HK
dc.identifier.hkuros80396en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037189712&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume62en_HK
dc.identifier.issue3en_HK
dc.identifier.spage173en_HK
dc.identifier.epage182en_HK
dc.identifier.isiWOS:000177306500002-
dc.publisher.placeSwitzerlanden_HK
dc.identifier.scopusauthoridBi, T=6602683764en_HK
dc.identifier.scopusauthoridNi, Y=7402910021en_HK
dc.identifier.scopusauthoridShen, CM=7402860197en_HK
dc.identifier.scopusauthoridWu, FF=7403465107en_HK

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