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Article: Hybrid fault section estimation system with radial basis function neural network and fuzzy system

TitleHybrid fault section estimation system with radial basis function neural network and fuzzy system
Authors
KeywordsCompanion fuzzy system
Fault section estimation
Power systems
Radial basis function neural networks
Retraining algorithm
Issue Date2005
PublisherZhongguo Dianji Gongcheng Xuehui. The Journal's web site is located at http://www.dwjs.com.cn
Citation
Zhongguo Dianji Gongcheng Xuebao/Proceedings Of The Chinese Society Of Electrical Engineering, 2005, v. 25 n. 14, p. 12-18 How to Cite?
AbstractIn this paper, functional equivalence between a radial basis function neural networks (RBF NN) and a companion fuzzy system (CFS) is built up throughout the neural network training process, therefore the black-box-like knowledge in a RBF NN will be rule-based and transparent in its CFS. Through useful knowledge extraction from the old CFS and insertion back to the new CFS piece by piece, the RBF NN retraining issue under network expansion and topology change can be solved effectively and efficiently. The corresponding FSE system has been implemented and tested in the IEEE 118-bus power system. The simulation results show that the suggested approach for RBF NN retraining works successfully and efficiently in the case of power network expansion and topology change, which significantly improves the application potential of RBF NN in FSE of practical power systems.
Persistent Identifierhttp://hdl.handle.net/10722/74112
ISSN
2015 SCImago Journal Rankings: 0.881
References

 

DC FieldValueLanguage
dc.contributor.authorBi, TSen_HK
dc.contributor.authorNi, YXen_HK
dc.contributor.authorWu, FLen_HK
dc.contributor.authorYang, QXen_HK
dc.date.accessioned2010-09-06T06:57:55Z-
dc.date.available2010-09-06T06:57:55Z-
dc.date.issued2005en_HK
dc.identifier.citationZhongguo Dianji Gongcheng Xuebao/Proceedings Of The Chinese Society Of Electrical Engineering, 2005, v. 25 n. 14, p. 12-18en_HK
dc.identifier.issn0258-8013en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74112-
dc.description.abstractIn this paper, functional equivalence between a radial basis function neural networks (RBF NN) and a companion fuzzy system (CFS) is built up throughout the neural network training process, therefore the black-box-like knowledge in a RBF NN will be rule-based and transparent in its CFS. Through useful knowledge extraction from the old CFS and insertion back to the new CFS piece by piece, the RBF NN retraining issue under network expansion and topology change can be solved effectively and efficiently. The corresponding FSE system has been implemented and tested in the IEEE 118-bus power system. The simulation results show that the suggested approach for RBF NN retraining works successfully and efficiently in the case of power network expansion and topology change, which significantly improves the application potential of RBF NN in FSE of practical power systems.en_HK
dc.languageengen_HK
dc.publisherZhongguo Dianji Gongcheng Xuehui. The Journal's web site is located at http://www.dwjs.com.cnen_HK
dc.relation.ispartofZhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineeringen_HK
dc.subjectCompanion fuzzy systemen_HK
dc.subjectFault section estimationen_HK
dc.subjectPower systemsen_HK
dc.subjectRadial basis function neural networksen_HK
dc.subjectRetraining algorithmen_HK
dc.titleHybrid fault section estimation system with radial basis function neural network and fuzzy systemen_HK
dc.typeArticleen_HK
dc.identifier.emailNi, YX: yxni@eee.hku.hken_HK
dc.identifier.authorityNi, YX=rp00161en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-23444445371en_HK
dc.identifier.hkuros119100en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-23444445371&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume25en_HK
dc.identifier.issue14en_HK
dc.identifier.spage12en_HK
dc.identifier.epage18en_HK
dc.publisher.placeChinaen_HK
dc.identifier.scopusauthoridBi, TS=6602683764en_HK
dc.identifier.scopusauthoridNi, YX=7402910021en_HK
dc.identifier.scopusauthoridWu, FL=7403465591en_HK
dc.identifier.scopusauthoridYang, QX=7404075866en_HK

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