Harmonic identification using parallel neural networks in single-phase systems

dc.contributorFed Univ Technol UTFPR
dc.contributorUniversidade Federal do ABC (UFABC)
dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T15:33:18Z
dc.date.available2014-05-20T15:33:18Z
dc.date.created2014-05-20T15:33:18Z
dc.date.issued2011-03-01
dc.identifierApplied Soft Computing. Amsterdam: Elsevier B.V., v. 11, n. 2, p. 2178-2185, 2011.
dc.identifier1568-4946
dc.identifierhttp://hdl.handle.net/11449/41969
dc.identifier10.1016/j.asoc.2010.07.017
dc.identifierWOS:000286373200070
dc.description.abstractIn this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationApplied Soft Computing
dc.relation3.907
dc.relation1,199
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectHarmonic distortion
dc.subjectNeural network application
dc.subjectSingle-phase power system
dc.subjectPower electronics
dc.titleHarmonic identification using parallel neural networks in single-phase systems
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución