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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/73076
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dc.contributor.authorSouza, André N.-
dc.contributor.authorDa Costa Jr., Pedro-
dc.contributor.authorDa Silva, Paulo S.-
dc.contributor.authorRamos, Caio C. O.-
dc.contributor.authorPapa, João Paulo-
dc.date.accessioned2014-05-27T11:26:20Z-
dc.date.accessioned2016-10-25T18:36:20Z-
dc.date.available2014-05-27T11:26:20Z-
dc.date.available2016-10-25T18:36:20Z-
dc.date.issued2011-12-21-
dc.identifierhttp://dx.doi.org/10.1109/ISAP.2011.6082204-
dc.identifier.citation2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011.-
dc.identifier.urihttp://hdl.handle.net/11449/73076-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/73076-
dc.description.abstractIn this paper we propose an accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the Time Domains Reflectometry method for signal acquisition, which was further analyzed by OPF and several other well known pattern recognition techniques. The results indicated that OPF and Support Vector Machines outperformed Artificial Neural Networks classifier. However, OPF has been much more efficient than all classifiers for training, and the second one faster for classification. © 2011 IEEE.en
dc.language.isoeng-
dc.sourceScopus-
dc.subjectFault Location-
dc.subjectOptimum-Path Forest-
dc.subjectPattern Recognition-
dc.subjectUnderground Systems-
dc.subjectArtificial Neural Network-
dc.subjectPattern recognition techniques-
dc.subjectReflectometry-
dc.subjectSignal acquisitions-
dc.subjectTime domain-
dc.subjectUnderground distribution system-
dc.subjectUnderground systems-
dc.subjectElectric fault location-
dc.subjectForestry-
dc.subjectIntelligent systems-
dc.subjectNeural networks-
dc.subjectPattern recognition-
dc.subjectPower transmission-
dc.subjectSignal processing-
dc.subjectTime domain analysis-
dc.subjectAlgorithms-
dc.subjectClassification-
dc.subjectDefects-
dc.subjectElectric Power Distribution-
dc.subjectForests-
dc.subjectNeural Networks-
dc.titleFault location in underground systems through optimum-path foresten
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.description.affiliationDepartment of Electrical Engineering UNESP - Univ. Estadual Paulista, São Paulo, São Paulo-
dc.description.affiliationDepartment of Electrical Engineering USP - University of São Paulo, São Paulo, São Paulo-
dc.description.affiliationDepartment of Computing UNESP - Univ. Estadual Paulista, Bauru, São Paulo-
dc.description.affiliationUnespDepartment of Electrical Engineering UNESP - Univ. Estadual Paulista, São Paulo, São Paulo-
dc.description.affiliationUnespDepartment of Computing UNESP - Univ. Estadual Paulista, Bauru, São Paulo-
dc.identifier.doi10.1109/ISAP.2011.6082204-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartof2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011-
dc.identifier.scopus2-s2.0-83655197667-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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