Neural approach for induction motor load torque identification in industrial applications

Detalhes bibliográficos
Autor(a) principal: Goedtel, Alessandro
Data de Publicação: 2007
Outros Autores: Silva, Ivan N. da, Serni, Paulo J. A. [UNESP], IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/195894
Resumo: Induction motors are widely used in several industrial sectors. However, the dimensioning of induction motors is often inaccurate because, in most cases, the load behavior in the shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for dimensioning induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since the proposed approach uses current, voltage and speed values as the only input parameters, one of its potentialities is related to the facility of hardware implementation for industrial environments and field applications. Simulation results are also presented to validate the proposed approach.
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spelling Neural approach for induction motor load torque identification in industrial applicationsInduction motors are widely used in several industrial sectors. However, the dimensioning of induction motors is often inaccurate because, in most cases, the load behavior in the shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for dimensioning induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since the proposed approach uses current, voltage and speed values as the only input parameters, one of its potentialities is related to the facility of hardware implementation for industrial environments and field applications. Simulation results are also presented to validate the proposed approach.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Sao Paulo, Dept Elect Engn, CP 359,Av Trabalhador Sancarlense 400, BR-13566590 Sao Carlos, SP, BrazilState Univ Sao Paulo, Elect Engn Dept, BR-17033360 Bauru, SP, BrazilState Univ Sao Paulo, Elect Engn Dept, BR-17033360 Bauru, SP, BrazilCNPq: 06/56093-3CNPq: 14236/2005-4IeeeUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Goedtel, AlessandroSilva, Ivan N. daSerni, Paulo J. A. [UNESP]IEEE2020-12-10T18:07:33Z2020-12-10T18:07:33Z2007-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1529-+Proceedings Of The 2007 Ieee Conference On Control Applications, Vols 1-3. New York: Ieee, p. 1529-+, 2007.1085-1992http://hdl.handle.net/11449/195894WOS:000253024000259Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings Of The 2007 Ieee Conference On Control Applications, Vols 1-3info:eu-repo/semantics/openAccess2024-06-28T13:34:43Zoai:repositorio.unesp.br:11449/195894Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:57:57.807369Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Neural approach for induction motor load torque identification in industrial applications
title Neural approach for induction motor load torque identification in industrial applications
spellingShingle Neural approach for induction motor load torque identification in industrial applications
Goedtel, Alessandro
title_short Neural approach for induction motor load torque identification in industrial applications
title_full Neural approach for induction motor load torque identification in industrial applications
title_fullStr Neural approach for induction motor load torque identification in industrial applications
title_full_unstemmed Neural approach for induction motor load torque identification in industrial applications
title_sort Neural approach for induction motor load torque identification in industrial applications
author Goedtel, Alessandro
author_facet Goedtel, Alessandro
Silva, Ivan N. da
Serni, Paulo J. A. [UNESP]
IEEE
author_role author
author2 Silva, Ivan N. da
Serni, Paulo J. A. [UNESP]
IEEE
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Goedtel, Alessandro
Silva, Ivan N. da
Serni, Paulo J. A. [UNESP]
IEEE
description Induction motors are widely used in several industrial sectors. However, the dimensioning of induction motors is often inaccurate because, in most cases, the load behavior in the shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for dimensioning induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since the proposed approach uses current, voltage and speed values as the only input parameters, one of its potentialities is related to the facility of hardware implementation for industrial environments and field applications. Simulation results are also presented to validate the proposed approach.
publishDate 2007
dc.date.none.fl_str_mv 2007-01-01
2020-12-10T18:07:33Z
2020-12-10T18:07:33Z
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 Proceedings Of The 2007 Ieee Conference On Control Applications, Vols 1-3. New York: Ieee, p. 1529-+, 2007.
1085-1992
http://hdl.handle.net/11449/195894
WOS:000253024000259
identifier_str_mv Proceedings Of The 2007 Ieee Conference On Control Applications, Vols 1-3. New York: Ieee, p. 1529-+, 2007.
1085-1992
WOS:000253024000259
url http://hdl.handle.net/11449/195894
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings Of The 2007 Ieee Conference On Control Applications, 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 1529-+
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv 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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