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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorGoedtel, A.-
Autor(es): dc.creatorSilva, I. N. da-
Autor(es): dc.creatorSerni, P. J. A. [UNESP]-
Autor(es): dc.creatorIEEE-
Data de aceite: dc.date.accessioned2022-02-22T00:07:08Z-
Data de disponibilização: dc.date.available2022-02-22T00:07:08Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2006-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/195869-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/195869-
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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionUniv Sao Paulo, Dept Elect Engn, EESC, Av Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Dept Elect Engn, UNESP, BR-17033360 Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Dept Elect Engn, UNESP, BR-17033360 Sao Carlos, SP, Brazil-
Descrição: dc.descriptionCNPq: 06/56093-3-
Descrição: dc.descriptionCNPq: 14236/2005-4-
Formato: dc.format918-+-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2006 Ieee International Conference On Power Electronic, Drives And Energy Systems, Vols 1 And 2-
???dc.source???: dc.sourceWeb of Science-
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-
Aparece nas coleções:Repositório Institucional - Unesp

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