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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorIEEE-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorGoedtel, A.-
Autor(es): dc.creatorDa Silva, I. N.-
Autor(es): dc.creatorSerni, P. J.A.-
Data de aceite: dc.date.accessioned2025-08-21T23:29:02Z-
Data de disponibilização: dc.date.available2025-08-21T23:29:02Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2006-12-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/PEDES.2006.344292-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/224947-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/224947-
Descrição: dc.descriptionInduction 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.-
Descrição: dc.descriptionIEEE-
Descrição: dc.descriptionElectrical Engineering Department (EESC) University of São Paulo (USP), Av. Trabalhador Sao-carlense, 400, CEP 13566-590, São Carlos, SP-
Descrição: dc.descriptionElectrical Engineering Department (DEE) State University of São Paulo (UNESP), CP 473, CEP 17033-360, Bauru, SP-
Descrição: dc.descriptionElectrical Engineering Department (DEE) State University of São Paulo (UNESP), CP 473, CEP 17033-360, Bauru, SP-
Idioma: dc.languageen-
Relação: dc.relation2006 International Conference on Power Electronics, Drives and Energy Systems, PEDES '06-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectInduction motors-
Palavras-chave: dc.subjectLoad modeling-
Palavras-chave: dc.subjectNeural networks-
Palavras-chave: dc.subjectParameter estimation-
Palavras-chave: dc.subjectSystem identification-
Título: dc.titleNeural approach for automatic identification of induction motor load torque in real-time industrial applications-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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