Load torque estimation in induction motors using artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Goedtel, A.
Data de Publicação: 2002
Outros Autores: da Silva, I. N., Serni, PJA, Avolio, E.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IJCNN.2002.1007717
http://hdl.handle.net/11449/35917
Resumo: The induction motors are largely used in several industry sectors. The dimensioning 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 paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.
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spelling Load torque estimation in induction motors using artificial neural networksThe induction motors are largely used in several industry sectors. The dimensioning 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 paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.State Univ São Paulo, FE, DEE, BR-17033360 Bauru, SP, BrazilState Univ São Paulo, FE, DEE, BR-17033360 Bauru, SP, BrazilInstitute of Electrical and Electronics Engineers (IEEE)Universidade Estadual Paulista (Unesp)Goedtel, A.da Silva, I. N.Serni, PJAAvolio, E.2014-05-20T15:25:30Z2014-05-20T15:25:30Z2002-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1379-1384http://dx.doi.org/10.1109/IJCNN.2002.1007717Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3. New York: IEEE, p. 1379-1384, 2002.1098-7576http://hdl.handle.net/11449/3591710.1109/IJCNN.2002.1007717WOS:000177402800246270572353521013448317899018238490000-0002-9984-9949Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3info:eu-repo/semantics/openAccess2024-06-28T13:34:43Zoai:repositorio.unesp.br:11449/35917Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:43:35.913775Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Load torque estimation in induction motors using artificial neural networks
title Load torque estimation in induction motors using artificial neural networks
spellingShingle Load torque estimation in induction motors using artificial neural networks
Goedtel, A.
title_short Load torque estimation in induction motors using artificial neural networks
title_full Load torque estimation in induction motors using artificial neural networks
title_fullStr Load torque estimation in induction motors using artificial neural networks
title_full_unstemmed Load torque estimation in induction motors using artificial neural networks
title_sort Load torque estimation in induction motors using artificial neural networks
author Goedtel, A.
author_facet Goedtel, A.
da Silva, I. N.
Serni, PJA
Avolio, E.
author_role author
author2 da Silva, I. N.
Serni, PJA
Avolio, E.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Goedtel, A.
da Silva, I. N.
Serni, PJA
Avolio, E.
description The induction motors are largely used in several industry sectors. The dimensioning 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 paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.
publishDate 2002
dc.date.none.fl_str_mv 2002-01-01
2014-05-20T15:25:30Z
2014-05-20T15:25:30Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/IJCNN.2002.1007717
Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3. New York: IEEE, p. 1379-1384, 2002.
1098-7576
http://hdl.handle.net/11449/35917
10.1109/IJCNN.2002.1007717
WOS:000177402800246
2705723535210134
4831789901823849
0000-0002-9984-9949
url http://dx.doi.org/10.1109/IJCNN.2002.1007717
http://hdl.handle.net/11449/35917
identifier_str_mv Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3. New York: IEEE, p. 1379-1384, 2002.
1098-7576
10.1109/IJCNN.2002.1007717
WOS:000177402800246
2705723535210134
4831789901823849
0000-0002-9984-9949
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1379-1384
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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