Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer

Autores
Montoya, Oscar Danilo; de Angelo, Cristian Hernan; Bossio, Guillermo Rubén
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This research addresses the parametric estimation problem in three-phase induction motors by applying a recently developed metaheuristic method known as the generalized normal distribution optimizer (GNDO). A nonlinear programming model based on the steady-state circuit of the induction motor, which uses its Thevenin equivalent, is employed to model the estimation problem. Estimation is carried out by minimizing the mean square error between torque data (obtained from measurements or provided by the manufacturer) and the values calculated with the model. The main advantage of using the GNDO is its effective balance between the exploration and exploitation of the solution space via Gaussian distributions. Numerical tests in two three-phase induction machines confirm the effectiveness of this approach in comparison with the classical genetic algorithm, the particle swarm optimizer, and the sine cosine algorithm. The GNDO approach reports objective function values of about 9.7834×10−14 and 2.6500×10−14, while the sine cosine algorithm reaches solutions of about 4.6327×10−10 and 1.2400×10−6 in both tested motors. All numerical simulations were performed in the MATLAB software, version 2022b.
Fil: Montoya, Oscar Danilo. Universidad Distrital Francisco José de Caldas; Colombia
Fil: de Angelo, Cristian Hernan. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; Argentina
Fil: Bossio, Guillermo Rubén. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; Argentina
Materia
Parametric estimation in induction motors
Generalized normal distribution optimizer
Mean square error minimization
Nonlinear programming via metaheuristic optimization
Torque calculation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/256651
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/256651
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizerMontoya, Oscar Danilode Angelo, Cristian HernanBossio, Guillermo RubénParametric estimation in induction motorsGeneralized normal distribution optimizerMean square error minimizationNonlinear programming via metaheuristic optimizationTorque calculationhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This research addresses the parametric estimation problem in three-phase induction motors by applying a recently developed metaheuristic method known as the generalized normal distribution optimizer (GNDO). A nonlinear programming model based on the steady-state circuit of the induction motor, which uses its Thevenin equivalent, is employed to model the estimation problem. Estimation is carried out by minimizing the mean square error between torque data (obtained from measurements or provided by the manufacturer) and the values calculated with the model. The main advantage of using the GNDO is its effective balance between the exploration and exploitation of the solution space via Gaussian distributions. Numerical tests in two three-phase induction machines confirm the effectiveness of this approach in comparison with the classical genetic algorithm, the particle swarm optimizer, and the sine cosine algorithm. The GNDO approach reports objective function values of about 9.7834×10−14 and 2.6500×10−14, while the sine cosine algorithm reaches solutions of about 4.6327×10−10 and 1.2400×10−6 in both tested motors. All numerical simulations were performed in the MATLAB software, version 2022b.Fil: Montoya, Oscar Danilo. Universidad Distrital Francisco José de Caldas; ColombiaFil: de Angelo, Cristian Hernan. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaFil: Bossio, Guillermo Rubén. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; ArgentinaElsevier2024-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/256651Montoya, Oscar Danilo; de Angelo, Cristian Hernan; Bossio, Guillermo Rubén; Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer; Elsevier; Results in Engineering; 23; 9-2024; 1-122590-1230CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2590123024007011info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rineng.2024.102446info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:29Zoai:ri.conicet.gov.ar:11336/256651instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:41:29.596CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
title Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
spellingShingle Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
Montoya, Oscar Danilo
Parametric estimation in induction motors
Generalized normal distribution optimizer
Mean square error minimization
Nonlinear programming via metaheuristic optimization
Torque calculation
title_short Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
title_full Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
title_fullStr Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
title_full_unstemmed Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
title_sort Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer
dc.creator.none.fl_str_mv Montoya, Oscar Danilo
de Angelo, Cristian Hernan
Bossio, Guillermo Rubén
author Montoya, Oscar Danilo
author_facet Montoya, Oscar Danilo
de Angelo, Cristian Hernan
Bossio, Guillermo Rubén
author_role author
author2 de Angelo, Cristian Hernan
Bossio, Guillermo Rubén
author2_role author
author
dc.subject.none.fl_str_mv Parametric estimation in induction motors
Generalized normal distribution optimizer
Mean square error minimization
Nonlinear programming via metaheuristic optimization
Torque calculation
topic Parametric estimation in induction motors
Generalized normal distribution optimizer
Mean square error minimization
Nonlinear programming via metaheuristic optimization
Torque calculation
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This research addresses the parametric estimation problem in three-phase induction motors by applying a recently developed metaheuristic method known as the generalized normal distribution optimizer (GNDO). A nonlinear programming model based on the steady-state circuit of the induction motor, which uses its Thevenin equivalent, is employed to model the estimation problem. Estimation is carried out by minimizing the mean square error between torque data (obtained from measurements or provided by the manufacturer) and the values calculated with the model. The main advantage of using the GNDO is its effective balance between the exploration and exploitation of the solution space via Gaussian distributions. Numerical tests in two three-phase induction machines confirm the effectiveness of this approach in comparison with the classical genetic algorithm, the particle swarm optimizer, and the sine cosine algorithm. The GNDO approach reports objective function values of about 9.7834×10−14 and 2.6500×10−14, while the sine cosine algorithm reaches solutions of about 4.6327×10−10 and 1.2400×10−6 in both tested motors. All numerical simulations were performed in the MATLAB software, version 2022b.
Fil: Montoya, Oscar Danilo. Universidad Distrital Francisco José de Caldas; Colombia
Fil: de Angelo, Cristian Hernan. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; Argentina
Fil: Bossio, Guillermo Rubén. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Tecnologías Energéticas y Materiales Avanzados; Argentina
description This research addresses the parametric estimation problem in three-phase induction motors by applying a recently developed metaheuristic method known as the generalized normal distribution optimizer (GNDO). A nonlinear programming model based on the steady-state circuit of the induction motor, which uses its Thevenin equivalent, is employed to model the estimation problem. Estimation is carried out by minimizing the mean square error between torque data (obtained from measurements or provided by the manufacturer) and the values calculated with the model. The main advantage of using the GNDO is its effective balance between the exploration and exploitation of the solution space via Gaussian distributions. Numerical tests in two three-phase induction machines confirm the effectiveness of this approach in comparison with the classical genetic algorithm, the particle swarm optimizer, and the sine cosine algorithm. The GNDO approach reports objective function values of about 9.7834×10−14 and 2.6500×10−14, while the sine cosine algorithm reaches solutions of about 4.6327×10−10 and 1.2400×10−6 in both tested motors. All numerical simulations were performed in the MATLAB software, version 2022b.
publishDate 2024
dc.date.none.fl_str_mv 2024-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/256651
Montoya, Oscar Danilo; de Angelo, Cristian Hernan; Bossio, Guillermo Rubén; Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer; Elsevier; Results in Engineering; 23; 9-2024; 1-12
2590-1230
CONICET Digital
CONICET
url http://hdl.handle.net/11336/256651
identifier_str_mv Montoya, Oscar Danilo; de Angelo, Cristian Hernan; Bossio, Guillermo Rubén; Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer; Elsevier; Results in Engineering; 23; 9-2024; 1-12
2590-1230
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2590123024007011
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rineng.2024.102446
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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