Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

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dc.contributor.authorDecanini, J. G. M. S.
dc.contributor.authorTonelli-Neto, M. S. [UNESP]
dc.contributor.authorMinussi, C. R. [UNESP]
dc.contributor.institutionInst Fed Educ Ciência & Tecnol São Paulo IFSP
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:29:09Z
dc.date.available2014-05-20T13:29:09Z
dc.date.issued2012-11-01
dc.description.abstractThe present study proposes a methodology for the automatic diagnosis of short-circuit faults in distribution systems using modern techniques for signal analysis and artificial intelligence. This support tool for decision making accelerates the restoration process, providing greater security, reliability and profitability to utilities. The fault detection procedure is performed using statistical and direct analyses of the current waveforms in the wavelet domain. Current and voltage signal features are extracted using discrete wavelet transform, multi-resolution analysis and energy concept. These behavioural indices correspond to the input vectors of three parallel sets of fuzzy ARTMAP neural networks. The network outcomes are integrated by the Dempster-Shafer theory, giving quantitative information about the diagnosis and its reliability. Tests were carried out using a practical distribution feeder from a Brazilian electric utility, and the results show that the method is efficient with a high level of confidence.en
dc.description.affiliationInst Fed Educ Ciência & Tecnol São Paulo IFSP, BR-19470000 Presidente Epitacio, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, UNESP, Dept Elect Engn, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, UNESP, Dept Elect Engn, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.extent1112-1120
dc.identifierhttp://dx.doi.org/10.1049/iet-gtd.2012.0028
dc.identifier.citationIet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 6, n. 11, p. 1112-1120, 2012.
dc.identifier.doi10.1049/iet-gtd.2012.0028
dc.identifier.issn1751-8687
dc.identifier.lattes7166279400544764
dc.identifier.urihttp://hdl.handle.net/11449/9798
dc.identifier.wosWOS:000318231300005
dc.language.isoeng
dc.publisherInst Engineering Technology-iet
dc.relation.ispartofIet Generation Transmission & Distribution
dc.relation.ispartofjcr2.618
dc.relation.ispartofsjr0,907
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.titleRobust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theoryen
dc.typeArtigo
dcterms.licensehttp://digital-library.theiet.org/journals/author-guide
dcterms.rightsHolderInst Engineering Technology-iet
unesp.author.lattes7166279400544764[3]
unesp.author.orcid0000-0001-6428-4506[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Ilha Solteirapt

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Artigos - Engenharia Elétrica - FEIS