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Publicação:
Fault location in underground systems using artificial neural networks and PSCAD/EMTDC

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dc.contributor.authorGastaldello, D. S. [UNESP]
dc.contributor.authorSouza, A. N. [UNESP]
dc.contributor.authorRamos, C. C O
dc.contributor.authorDa Costa Junior, P. [UNESP]
dc.contributor.authorZago, M. G.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionFaculdade de Tecnologia do Estado de São Paulo (FATEC)
dc.date.accessioned2014-05-27T11:27:04Z
dc.date.available2014-05-27T11:27:04Z
dc.date.issued2012-10-01
dc.description.abstractThe need for high reliability and environmental concerns are making the underground networks the most appropriate choice of energy distribution. However, like any other system, underground distribution systems are not free of failures. In this context, this work presents an approach to study underground systems using computational tools by integrating the software PSCAD/EMTDC with artificial neural networks to assist fault location in power distribution systems. Targeted benefits include greater accuracy and reduced repair time. The results presented here shows the feasibility of the proposed approach. © 2012 IEEE.en
dc.description.affiliationUNESP - Univ. Estadual Paulista Department of Electrical Engineering, Bauru
dc.description.affiliationUniversity of São Paulo Department of Electrical Engineering, São Paulo
dc.description.affiliationFATEC Department of Electrical Engineering, Bauru
dc.description.affiliationUnespUNESP - Univ. Estadual Paulista Department of Electrical Engineering, Bauru
dc.format.extent423-427
dc.identifierhttp://dx.doi.org/10.1109/INES.2012.6249871
dc.identifier.citationINES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings, p. 423-427.
dc.identifier.doi10.1109/INES.2012.6249871
dc.identifier.lattes8212775960494686
dc.identifier.lattes8424547290253539
dc.identifier.orcid0000-0003-1495-633X
dc.identifier.scopus2-s2.0-84866688600
dc.identifier.urihttp://hdl.handle.net/11449/73613
dc.language.isoeng
dc.relation.ispartofINES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectComputational tools
dc.subjectEnergy distributions
dc.subjectEnvironmental concerns
dc.subjectHigh reliability
dc.subjectPower distribution system
dc.subjectPSCAD/EMTDC
dc.subjectUnderground distribution system
dc.subjectUnderground networks
dc.subjectUnderground systems
dc.subjectElectric load distribution
dc.subjectNeural networks
dc.titleFault location in underground systems using artificial neural networks and PSCAD/EMTDCen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dspace.entity.typePublication
unesp.author.lattes8212775960494686[2]
unesp.author.lattes8424547290253539[4]
unesp.author.orcid0000-0003-1495-633X[4]
unesp.author.orcid0000-0002-8617-5404[2]
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Engenharia, Baurupt
unesp.departmentEngenharia Elétrica - FEBpt

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Bauru - FEB - Faculdade de Engenharia