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Article: An evaluation of artificial intelligent technologiesfor fault diagnosis in power network

TitleAn evaluation of artificial intelligent technologiesfor fault diagnosis in power network
人工智能技術在輸電網絡故障診斷中的應用述評
Authors
KeywordsFault diagnosis (故障診斷)
Expert system (專家系統)
Artificial neural network (人工神經網絡)
Fuzzy theory (模糊理論)
Genetic algorithms (遺傳算法)
Issue Date2000
PublisherDianli Xitong Zidonghua Zazhishe (電力系統自動化雜誌社). The Journal's web site is located at http://www.aeps-info.com/aeps/ch/index.aspx
Citation
Automation of Electric power Systems, 2000, v. 24 n. 2, p. 11-16 How to Cite?
電力系統自動化, 2000, v. 24 n. 2, p. 11-16 How to Cite?
AbstractThe artificial intelligent technology provides an advanced tool for fault diagnosis in powel networks, and has madegreat progress in the past thirty years. This paper briefly introduces the basic concepts of the intelligent technologies for faultdiagnosis. including expert system, artificial neural network, fuzzy theory, genetic algorithms and Petri net. Based on theseconcepts. the corresponding methods for power network fault diagnosis in the literature are reviewed. Their features andmain problems are discussed for further research and development. 簡要介紹了相關的人工智能技術 ,如專家系統 ( expert system) ,人工神經網絡 ( artificialneural network) ,模糊理論 ( fuzzy theory) ,遺傳算法 ( genetic algorithms)及 Petri網絡 ( Petri net)等的基本概念 ,并在此基礎上對文獻中提出的相應的輸電網絡故障診斷方法進行述評 ,分析它們在輸電網絡故障診斷中應用的特點以及存在的主要問題 ,以促進該研究領域的進一步發展
Persistent Identifierhttp://hdl.handle.net/10722/73678
ISSN
2015 SCImago Journal Rankings: 0.772

 

DC FieldValueLanguage
dc.contributor.authorBi, T-
dc.contributor.authorNi, Y-
dc.contributor.authorYang, Q-
dc.date.accessioned2010-09-06T06:53:42Z-
dc.date.available2010-09-06T06:53:42Z-
dc.date.issued2000-
dc.identifier.citationAutomation of Electric power Systems, 2000, v. 24 n. 2, p. 11-16-
dc.identifier.citation電力系統自動化, 2000, v. 24 n. 2, p. 11-16-
dc.identifier.issn1000-1026-
dc.identifier.urihttp://hdl.handle.net/10722/73678-
dc.description.abstractThe artificial intelligent technology provides an advanced tool for fault diagnosis in powel networks, and has madegreat progress in the past thirty years. This paper briefly introduces the basic concepts of the intelligent technologies for faultdiagnosis. including expert system, artificial neural network, fuzzy theory, genetic algorithms and Petri net. Based on theseconcepts. the corresponding methods for power network fault diagnosis in the literature are reviewed. Their features andmain problems are discussed for further research and development. 簡要介紹了相關的人工智能技術 ,如專家系統 ( expert system) ,人工神經網絡 ( artificialneural network) ,模糊理論 ( fuzzy theory) ,遺傳算法 ( genetic algorithms)及 Petri網絡 ( Petri net)等的基本概念 ,并在此基礎上對文獻中提出的相應的輸電網絡故障診斷方法進行述評 ,分析它們在輸電網絡故障診斷中應用的特點以及存在的主要問題 ,以促進該研究領域的進一步發展-
dc.languagechi-
dc.publisherDianli Xitong Zidonghua Zazhishe (電力系統自動化雜誌社). The Journal's web site is located at http://www.aeps-info.com/aeps/ch/index.aspx-
dc.relation.ispartofAutomation of Electric power Systems-
dc.relation.ispartof電力系統自動化-
dc.subjectFault diagnosis (故障診斷)-
dc.subjectExpert system (專家系統)-
dc.subjectArtificial neural network (人工神經網絡)-
dc.subjectFuzzy theory (模糊理論)-
dc.subjectGenetic algorithms (遺傳算法)-
dc.titleAn evaluation of artificial intelligent technologiesfor fault diagnosis in power network-
dc.title人工智能技術在輸電網絡故障診斷中的應用述評-
dc.typeArticle-
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1000-1026&volume=24&issue=2&spage=11&epage=16&date=2000&atitle=A+survey+of+AI+Technology+applications+in+transmission+network+fault+diagnosisen_HK
dc.identifier.emailNi, Y: yxni@eee.hku.hk-
dc.identifier.authorityNi, Y=rp00161-
dc.identifier.hkuros53925-
dc.identifier.volume24-
dc.identifier.issue2-
dc.identifier.spage11-
dc.identifier.epage16-
dc.publisher.placeChina (中國)-

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