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Conference Paper: Advanced Fault Section Estimation System for Power Networks Based on Hybrid Fuzzy System and Radial Basis Function Neural Network

TitleAdvanced Fault Section Estimation System for Power Networks Based on Hybrid Fuzzy System and Radial Basis Function Neural Network
Authors
KeywordsFault Section Estimation
Fuzzy System
Radial Basis Function Neural Network
Retraining Strategy
Power Networks
Issue Date2001
PublisherIEEE.
Citation
The 33rd Annual North American Power Symposium, College Station, USA, October 15-16, 2001, p. 99-104 How to Cite?
AbstractAbstract Although the radial basis function neural network (RBF NN) offers a potential solution for fault section estimation (FSE) in power networks, it has to be totally retrained for the case of power network topology change or power network expansion and cannot provide any explanations for its diagnosis results due to the blackbox nature of the neural network. In this paper, the functional equivalence between RBF NN and fuzzy system (FS) is built up for FSE problem throughout the neural network training process. Furthermore, based on this point, a novel retraining strategy is presented for RBF NN, which can extract the unchanged knowledge from the original RBF NN and then insert the knowledge back to the new RBF NN about the changing part of the power network in the case of network topology change or expansion. The retraining strategy has been implemented and tested in a 4-bus power system. The simulation results show that the advanced FSE system with hybrid FS and RBF NN works successfully and efficiently in power networks.
Persistent Identifierhttp://hdl.handle.net/10722/57286
ISSN

 

DC FieldValueLanguage
dc.contributor.authorBi, Ten_HK
dc.contributor.authorNi, Yen_HK
dc.contributor.authorWu, FFen_HK
dc.date.accessioned2010-04-12T01:31:46Z-
dc.date.available2010-04-12T01:31:46Z-
dc.date.issued2001en_HK
dc.identifier.citationThe 33rd Annual North American Power Symposium, College Station, USA, October 15-16, 2001, p. 99-104en_HK
dc.identifier.issn0895-4097en_HK
dc.identifier.urihttp://hdl.handle.net/10722/57286-
dc.description.abstractAbstract Although the radial basis function neural network (RBF NN) offers a potential solution for fault section estimation (FSE) in power networks, it has to be totally retrained for the case of power network topology change or power network expansion and cannot provide any explanations for its diagnosis results due to the blackbox nature of the neural network. In this paper, the functional equivalence between RBF NN and fuzzy system (FS) is built up for FSE problem throughout the neural network training process. Furthermore, based on this point, a novel retraining strategy is presented for RBF NN, which can extract the unchanged knowledge from the original RBF NN and then insert the knowledge back to the new RBF NN about the changing part of the power network in the case of network topology change or expansion. The retraining strategy has been implemented and tested in a 4-bus power system. The simulation results show that the advanced FSE system with hybrid FS and RBF NN works successfully and efficiently in power networks.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rights©2001 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectFault Section Estimationen_HK
dc.subjectFuzzy Systemen_HK
dc.subjectRadial Basis Function Neural Networken_HK
dc.subjectRetraining Strategyen_HK
dc.subjectPower Networksen_HK
dc.titleAdvanced Fault Section Estimation System for Power Networks Based on Hybrid Fuzzy System and Radial Basis Function Neural Networken_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0895-4097&volume=&spage=99&epage=104&date=2001&atitle=Advanced+Fault+Section+Estimation+System+for+Power+Networks+Based+on+Hybrid+Fuzzy+System+and+Radial+Basis+Function+Neural+Networken_HK
dc.identifier.emailNi, Y: yxni@eee.hku.hken_HK
dc.identifier.emailWu, FF: ffwu@eee.hku.hken_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.hkuros73339-

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