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Conference Paper: A novel radial basis function neural network for fault section estimation in transmission network

TitleA novel radial basis function neural network for fault section estimation in transmission network
Authors
Bi, TSNi, YXShen, CMWu, FFYang, QX
Issue Date2001
PublisherIEEE.
Citation
The 5th International Conference on Advances in Power System Control, Operation and Management, Hong Kong, China, 30 October - 1 November 2000, v. 1, p. 259-263 How to Cite?
DOI: http://dx.doi.org/10.1049/cp:20000403
AbstractIn this paper, the application of Radial Basis Function Neural Network (RBF NN) to fault section estimation in power systems is addressed. The orthogonal least square algorithm has been extended to optimize the parameters of RBF NN. In order to assess the effectiveness of RBF NN, a classical Back-Propagation Neural Network (BP NN) has been developed to solve the same problem for comparison. Computer test is conducted on a 4-bus test system and the test results show that the RBF NN is quite effective and superior to BP NN in fault section estimation.
Persistent Identifierhttp://hdl.handle.net/10722/46338
ISBN
0-85296-791-8
ISSN
0537-9989
2019 SCImago Journal Rankings: 0.101
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorBi, TSen_HK
dc.contributor.authorNi, YXen_HK
dc.contributor.authorShen, CMen_HK
dc.contributor.authorWu, FFen_HK
dc.contributor.authorYang, QXen_HK
dc.date.accessioned2007-10-30T06:47:40Z-
dc.date.available2007-10-30T06:47:40Z-
dc.date.issued2001en_HK
dc.identifier.citationThe 5th International Conference on Advances in Power System Control, Operation and Management, Hong Kong, China, 30 October - 1 November 2000, v. 1, p. 259-263en_HK
dc.identifier.isbn0-85296-791-8en_HK
dc.identifier.issn0537-9989en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46338-
dc.description.abstractIn this paper, the application of Radial Basis Function Neural Network (RBF NN) to fault section estimation in power systems is addressed. The orthogonal least square algorithm has been extended to optimize the parameters of RBF NN. In order to assess the effectiveness of RBF NN, a classical Back-Propagation Neural Network (BP NN) has been developed to solve the same problem for comparison. Computer test is conducted on a 4-bus test system and the test results show that the RBF NN is quite effective and superior to BP NN in fault section estimation.en_HK
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dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEE Conference Publicationen_HK
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.titleA novel radial basis function neural network for fault section estimation in transmission networken_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.doi10.1049/cp:20000403-
dc.identifier.scopuseid_2-s2.0-0035761486en_HK
dc.identifier.hkuros73293-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035761486&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1-
dc.identifier.spage259en_HK
dc.identifier.epage263en_HK
dc.identifier.scopusauthoridBi, TS=6602683764en_HK
dc.identifier.scopusauthoridNi, YX=7402910021en_HK
dc.identifier.scopusauthoridShen, CM=7402859778en_HK
dc.identifier.scopusauthoridWu, FF=7403465107en_HK
dc.identifier.scopusauthoridYang, QX=7404075866en_HK
dc.identifier.issnl0537-9989-

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