Adjustment of model parameters to estimate distribution transformers remaining lifespan

Autores
Jimenez, Victor Adrian; Will, Adrian L. E.; Gotay Sardiñas, Jorge; Rodriguez, Sebastian Alberto
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.
Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina
Fil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina
Fil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina
Fil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina
Materia
DISTRIBUTION TRANSFORMER
THERMAL MODEL
TRANSFORMER LIFESPAN
PARAMETERS ADJUSTMENT
GENETIC ALGORITHMS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/81247
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Adjustment of model parameters to estimate distribution transformers remaining lifespanJimenez, Victor AdrianWill, Adrian L. E.Gotay Sardiñas, JorgeRodriguez, Sebastian AlbertoDISTRIBUTION TRANSFORMERTHERMAL MODELTRANSFORMER LIFESPANPARAMETERS ADJUSTMENTGENETIC ALGORITHMShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaScientific Research Publishing Inc.2018-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/81247Jimenez, Victor Adrian; Will, Adrian L. E.; Gotay Sardiñas, Jorge; Rodriguez, Sebastian Alberto; Adjustment of model parameters to estimate distribution transformers remaining lifespan; Scientific Research Publishing Inc.; Smart Grid and Renewable Energy; 09; 09; 9-2018; 151-1702151-481X2151-4844CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.scirp.org/pdf/SGRE_2018092816092325.pdfinfo:eu-repo/semantics/altIdentifier/doi/10.4236/sgre.2018.99010info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:03:29Zoai:ri.conicet.gov.ar:11336/81247instacron: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:34982025-09-10 13:03:30.06CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Adjustment of model parameters to estimate distribution transformers remaining lifespan
title Adjustment of model parameters to estimate distribution transformers remaining lifespan
spellingShingle Adjustment of model parameters to estimate distribution transformers remaining lifespan
Jimenez, Victor Adrian
DISTRIBUTION TRANSFORMER
THERMAL MODEL
TRANSFORMER LIFESPAN
PARAMETERS ADJUSTMENT
GENETIC ALGORITHMS
title_short Adjustment of model parameters to estimate distribution transformers remaining lifespan
title_full Adjustment of model parameters to estimate distribution transformers remaining lifespan
title_fullStr Adjustment of model parameters to estimate distribution transformers remaining lifespan
title_full_unstemmed Adjustment of model parameters to estimate distribution transformers remaining lifespan
title_sort Adjustment of model parameters to estimate distribution transformers remaining lifespan
dc.creator.none.fl_str_mv Jimenez, Victor Adrian
Will, Adrian L. E.
Gotay Sardiñas, Jorge
Rodriguez, Sebastian Alberto
author Jimenez, Victor Adrian
author_facet Jimenez, Victor Adrian
Will, Adrian L. E.
Gotay Sardiñas, Jorge
Rodriguez, Sebastian Alberto
author_role author
author2 Will, Adrian L. E.
Gotay Sardiñas, Jorge
Rodriguez, Sebastian Alberto
author2_role author
author
author
dc.subject.none.fl_str_mv DISTRIBUTION TRANSFORMER
THERMAL MODEL
TRANSFORMER LIFESPAN
PARAMETERS ADJUSTMENT
GENETIC ALGORITHMS
topic DISTRIBUTION TRANSFORMER
THERMAL MODEL
TRANSFORMER LIFESPAN
PARAMETERS ADJUSTMENT
GENETIC ALGORITHMS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.
Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina
Fil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina
Fil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina
Fil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina
description Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
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/11336/81247
Jimenez, Victor Adrian; Will, Adrian L. E.; Gotay Sardiñas, Jorge; Rodriguez, Sebastian Alberto; Adjustment of model parameters to estimate distribution transformers remaining lifespan; Scientific Research Publishing Inc.; Smart Grid and Renewable Energy; 09; 09; 9-2018; 151-170
2151-481X
2151-4844
CONICET Digital
CONICET
url http://hdl.handle.net/11336/81247
identifier_str_mv Jimenez, Victor Adrian; Will, Adrian L. E.; Gotay Sardiñas, Jorge; Rodriguez, Sebastian Alberto; Adjustment of model parameters to estimate distribution transformers remaining lifespan; Scientific Research Publishing Inc.; Smart Grid and Renewable Energy; 09; 09; 9-2018; 151-170
2151-481X
2151-4844
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.scirp.org/pdf/SGRE_2018092816092325.pdf
info:eu-repo/semantics/altIdentifier/doi/10.4236/sgre.2018.99010
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Scientific Research Publishing Inc.
publisher.none.fl_str_mv Scientific Research Publishing Inc.
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
_version_ 1842980087459217408
score 12.993085

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