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dc.contributor.authorOba Ramos, Caio Cesar-
dc.contributor.authorde Sousa, Andra Nunes-
dc.contributor.authorPapa, João Paulo-
dc.contributor.authorFalcao, Alexandre Xavier-
dc.date.accessioned2014-05-20T13:25:59Z-
dc.date.accessioned2016-10-25T16:46:13Z-
dc.date.available2014-05-20T13:25:59Z-
dc.date.available2016-10-25T16:46:13Z-
dc.date.issued2011-02-01-
dc.identifierhttp://dx.doi.org/10.1109/TPWRS.2010.2051823-
dc.identifier.citationIEEE Transactions on Power Systems. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 26, n. 1, p. 181-189, 2011.-
dc.identifier.issn0885-8950-
dc.identifier.urihttp://hdl.handle.net/11449/8303-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/8303-
dc.description.abstractNowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.en
dc.format.extent181-189-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
dc.sourceWeb of Science-
dc.subjectNontechnical lossesen
dc.subjectoptimum-path foresten
dc.subjectpattern recognitionen
dc.titleA New Approach for Nontechnical Losses Detection Based on Optimum-Path Foresten
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.description.affiliationSão Paulo State Univ, Dept Elect Engn, Intelligent Tech & Power Syst Lab, São Paulo, Brazil-
dc.description.affiliationSão Paulo State Univ, Dept Comp, São Paulo, Brazil-
dc.description.affiliationUniv Estadual Campinas, Inst Comp, São Paulo, Brazil-
dc.description.affiliationSão Paulo State Univ, Dept Comp Sci, São Paulo, Brazil-
dc.description.affiliationUnespSão Paulo State Univ, Dept Elect Engn, Intelligent Tech & Power Syst Lab, São Paulo, Brazil-
dc.description.affiliationUnespSão Paulo State Univ, Dept Comp, São Paulo, Brazil-
dc.description.affiliationUnespSão Paulo State Univ, Dept Comp Sci, São Paulo, Brazil-
dc.identifier.doi10.1109/TPWRS.2010.2051823-
dc.identifier.wosWOS:000286516100021-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofIEEE Transactions on Power Systems-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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