Neural approach for automatic identification of induction motor load torque in real-time industrial applications

Detalhes bibliográficos
Autor(a) principal: Goedtel, A.
Data de Publicação: 2006
Outros Autores: Silva, I. N. da, Serni, P. 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/11449/195869
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 automatic identification of induction motor load torque in real-time industrial applicationsinduction motorsload modelingneural networksparameter estimationsystem identificationInduction 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, EESC, Av Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP, BrazilUniv Sao Paulo, Dept Elect Engn, UNESP, BR-17033360 Sao Carlos, SP, BrazilUniv Sao Paulo, Dept Elect Engn, UNESP, BR-17033360 Sao Carlos, SP, BrazilCNPq: 06/56093-3CNPq: 14236/2005-4IeeeUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Goedtel, A.Silva, I. N. daSerni, P. J. A. [UNESP]IEEE2020-12-10T18:06:04Z2020-12-10T18:06:04Z2006-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject918-+2006 Ieee International Conference On Power Electronic, Drives And Energy Systems, Vols 1 And 2. New York: Ieee, p. 918-+, 2006.http://hdl.handle.net/11449/195869WOS:000245596300169Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2006 Ieee International Conference On Power Electronic, Drives And Energy Systems, Vols 1 And 2info:eu-repo/semantics/openAccess2021-10-23T12:19:11Zoai:repositorio.unesp.br:11449/195869Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T12:19:11Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Neural approach for automatic identification of induction motor load torque in real-time industrial applications
title Neural approach for automatic identification of induction motor load torque in real-time industrial applications
spellingShingle Neural approach for automatic identification of induction motor load torque in real-time industrial applications
Goedtel, A.
induction motors
load modeling
neural networks
parameter estimation
system identification
title_short Neural approach for automatic identification of induction motor load torque in real-time industrial applications
title_full Neural approach for automatic identification of induction motor load torque in real-time industrial applications
title_fullStr Neural approach for automatic identification of induction motor load torque in real-time industrial applications
title_full_unstemmed Neural approach for automatic identification of induction motor load torque in real-time industrial applications
title_sort Neural approach for automatic identification of induction motor load torque in real-time industrial applications
author Goedtel, A.
author_facet Goedtel, A.
Silva, I. N. da
Serni, P. J. A. [UNESP]
IEEE
author_role author
author2 Silva, I. N. da
Serni, P. 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, A.
Silva, I. N. da
Serni, P. J. A. [UNESP]
IEEE
dc.subject.por.fl_str_mv induction motors
load modeling
neural networks
parameter estimation
system identification
topic induction motors
load modeling
neural networks
parameter estimation
system identification
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 2006
dc.date.none.fl_str_mv 2006-01-01
2020-12-10T18:06:04Z
2020-12-10T18:06:04Z
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 2006 Ieee International Conference On Power Electronic, Drives And Energy Systems, Vols 1 And 2. New York: Ieee, p. 918-+, 2006.
http://hdl.handle.net/11449/195869
WOS:000245596300169
identifier_str_mv 2006 Ieee International Conference On Power Electronic, Drives And Energy Systems, Vols 1 And 2. New York: Ieee, p. 918-+, 2006.
WOS:000245596300169
url http://hdl.handle.net/11449/195869
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2006 Ieee International Conference On Power Electronic, Drives And Energy Systems, Vols 1 And 2
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 918-+
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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