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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
Title | Advanced Fault Section Estimation System for Power Networks Based on Hybrid Fuzzy System and Radial Basis Function Neural Network |
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Authors | |
Keywords | Fault Section Estimation Fuzzy System Radial Basis Function Neural Network Retraining Strategy Power Networks |
Issue Date | 2001 |
Publisher | IEEE. |
Citation | The 33rd Annual North American Power Symposium, College Station, USA, October 15-16, 2001, p. 99-104 How to Cite? |
Abstract | Abstract 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 Identifier | http://hdl.handle.net/10722/57286 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Bi, T | en_HK |
dc.contributor.author | Ni, Y | en_HK |
dc.contributor.author | Wu, FF | en_HK |
dc.date.accessioned | 2010-04-12T01:31:46Z | - |
dc.date.available | 2010-04-12T01:31:46Z | - |
dc.date.issued | 2001 | en_HK |
dc.identifier.citation | The 33rd Annual North American Power Symposium, College Station, USA, October 15-16, 2001, p. 99-104 | en_HK |
dc.identifier.issn | 0895-4097 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/57286 | - |
dc.description.abstract | Abstract 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.language | eng | en_HK |
dc.publisher | IEEE. | 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.subject | Fault Section Estimation | en_HK |
dc.subject | Fuzzy System | en_HK |
dc.subject | Radial Basis Function Neural Network | en_HK |
dc.subject | Retraining Strategy | en_HK |
dc.subject | Power Networks | en_HK |
dc.title | Advanced Fault Section Estimation System for Power Networks Based on Hybrid Fuzzy System and Radial Basis Function Neural Network | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.openurl | http://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+Network | en_HK |
dc.identifier.email | Ni, Y: yxni@eee.hku.hk | en_HK |
dc.identifier.email | Wu, FF: ffwu@eee.hku.hk | en_HK |
dc.description.nature | published_or_final_version | en_HK |
dc.identifier.hkuros | 73339 | - |
dc.identifier.issnl | 0895-4097 | - |