dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorPolytechnic Institute of Porto-IPP
dc.contributorUniversidade de São Paulo (USP)
dc.date.accessioned2014-05-27T11:30:15Z
dc.date.available2014-05-27T11:30:15Z
dc.date.created2014-05-27T11:30:15Z
dc.date.issued2013-08-26
dc.identifier2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013.
dc.identifierhttp://hdl.handle.net/11449/76325
dc.identifier10.1109/ISGT-LA.2013.6554383
dc.identifierWOS:000326589900015
dc.identifier2-s2.0-84882308363
dc.identifier9039182932747194
dc.description.abstractThe non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.
dc.languageeng
dc.relation2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectCharged System Search
dc.subjectNeural Networks
dc.subjectNontechnical Losses
dc.subjectCharged system searches
dc.subjectCompetitive environment
dc.subjectMeta-heuristic techniques
dc.subjectMulti-layer perceptron neural networks
dc.subjectNon-technical loss
dc.subjectOptimization techniques
dc.subjectPower distribution system
dc.subjectTrivial solutions
dc.subjectElectric load distribution
dc.subjectElectric utilities
dc.subjectPrivatization
dc.subjectSmart power grids
dc.subjectNeural networks
dc.titleMultilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection
dc.typeActas de congresos


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