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dc.contributor.authorGoedtel, A.
dc.contributor.authorDa Silva, I. N.
dc.contributor.authorSerni, PJA
dc.date.accessioned2014-05-20T13:27:14Z
dc.date.available2014-05-20T13:27:14Z
dc.date.issued2005-04-01
dc.identifierhttp://dx.doi.org/10.1080/15325000590479910
dc.identifier.citationElectric Power Components and Systems. Philadelphia: Taylor & Francis Inc., v. 33, n. 4, p. 363-387, 2005.
dc.identifier.issn1532-5008
dc.identifier.urihttp://hdl.handle.net/11449/8908
dc.description.abstractInduction motors are largely used in several industry sectors. The selection of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this article is to use artificial neural networks for torque estimation with the purpose of best selecting the induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since proposed approach estimates the torque behavior from the transient to the steady state, one of its main contributions is the potential to also be implemented in control schemes for real-time applications. Simulation results are also presented to validate the proposed approach.en
dc.format.extent363-387
dc.language.isoeng
dc.publisherTaylor & Francis Inc
dc.relation.ispartofElectric Power Components and Systems
dc.sourceWeb of Science
dc.subjectinduction motorspt
dc.subjectload modelingpt
dc.subjectneural networkspt
dc.subjectparameter estimationpt
dc.subjectsystem identificationpt
dc.titleNeural network based estimation of torque in induction motors for real-time applicationsen
dc.typeArtigo
dcterms.licensehttp://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp
dcterms.rightsHolderTaylor & Francis Inc
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.description.affiliationUNESP, Dept Elect Engn, BR-17033360 Bauru, SP, Brazil
dc.description.affiliationUnespUNESP, Dept Elect Engn, BR-17033360 Bauru, SP, Brazil
dc.identifier.doi10.1080/15325000590479910
dc.identifier.wosWOS:000227145300001
dc.rights.accessRightsAcesso restrito
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Baurupt
dc.identifier.lattes4831789901823849
dc.identifier.orcid0000-0002-9984-9949
unesp.author.lattes4831789901823849[3]
unesp.author.orcid0000-0002-1296-5454[2]
unesp.author.orcid0000-0002-9984-9949[3]
dc.relation.ispartofjcr1.144
dc.relation.ispartofsjr0,373
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