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: Da Silva, I. N., Serni, P. J.A. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PEDES.2006.344292
http://hdl.handle.net/11449/224947
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. ©2006 IEEE.
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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. ©2006 IEEE.IEEEElectrical Engineering Department (EESC) University of São Paulo (USP), Av. Trabalhador Sao-carlense, 400, CEP 13566-590, São Carlos, SPElectrical Engineering Department (DEE) State University of São Paulo (UNESP), CP 473, CEP 17033-360, Bauru, SPElectrical Engineering Department (DEE) State University of São Paulo (UNESP), CP 473, CEP 17033-360, Bauru, SPIEEEUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)Goedtel, A.Da Silva, I. N.Serni, P. J.A. [UNESP]2022-04-28T20:18:49Z2022-04-28T20:18:49Z2006-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PEDES.2006.3442922006 International Conference on Power Electronics, Drives and Energy Systems, PEDES '06.http://hdl.handle.net/11449/22494710.1109/PEDES.2006.3442922-s2.0-34547563577Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2006 International Conference on Power Electronics, Drives and Energy Systems, PEDES '06info:eu-repo/semantics/openAccess2024-06-28T13:34:43Zoai:repositorio.unesp.br:11449/224947Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T00:02:34.836033Repositório 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.
Da Silva, I. N.
Serni, P. J.A. [UNESP]
author_role author
author2 Da Silva, I. N.
Serni, P. J.A. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv IEEE
Universidade de São Paulo (USP)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Goedtel, A.
Da Silva, I. N.
Serni, P. J.A. [UNESP]
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. ©2006 IEEE.
publishDate 2006
dc.date.none.fl_str_mv 2006-12-01
2022-04-28T20:18:49Z
2022-04-28T20:18:49Z
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/PEDES.2006.344292
2006 International Conference on Power Electronics, Drives and Energy Systems, PEDES '06.
http://hdl.handle.net/11449/224947
10.1109/PEDES.2006.344292
2-s2.0-34547563577
url http://dx.doi.org/10.1109/PEDES.2006.344292
http://hdl.handle.net/11449/224947
identifier_str_mv 2006 International Conference on Power Electronics, Drives and Energy Systems, PEDES '06.
10.1109/PEDES.2006.344292
2-s2.0-34547563577
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2006 International Conference on Power Electronics, Drives and Energy Systems, PEDES '06
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
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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