Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks

Producción Científica

Detalhes bibliográficos
Autores: Merizalde Zamora, Yury Humberto, Hernández Callejo, Luis, Duque Pérez, Óscar, Alonso Gómez, Víctor
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/59537
Acesso em linha:https://doi.org/10.3390/app11156942
https://uvadoc.uva.es/handle/10324/59537
Access Level:acceso abierto
Palavra-chave:Wind turbines
Artificial intelligence
Inteligencia artificial
Motores de inducción
Faults diagnostic
Synthetic data
3313.30 Turbinas
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spelling Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networksMerizalde Zamora, Yury HumbertoHernández Callejo, LuisDuque Pérez, ÓscarAlonso Gómez, VíctorWind turbinesArtificial intelligenceInteligencia artificialMotores de inducciónFaults diagnosticSynthetic data3313.30 TurbinasProducción CientíficaTo ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state.MDPI2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/app11156942https://uvadoc.uva.es/handle/10324/59537reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.mdpi.com/2076-3417/11/15/6942info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:uvadoc.uva.es:10324/595372026-06-13T12:44:47Z
dc.title.none.fl_str_mv Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
title Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
spellingShingle Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
Merizalde Zamora, Yury Humberto
Wind turbines
Artificial intelligence
Inteligencia artificial
Motores de inducción
Faults diagnostic
Synthetic data
3313.30 Turbinas
title_short Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
title_full Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
title_fullStr Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
title_full_unstemmed Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
title_sort Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
dc.creator.none.fl_str_mv Merizalde Zamora, Yury Humberto
Hernández Callejo, Luis
Duque Pérez, Óscar
Alonso Gómez, Víctor
author Merizalde Zamora, Yury Humberto
author_facet Merizalde Zamora, Yury Humberto
Hernández Callejo, Luis
Duque Pérez, Óscar
Alonso Gómez, Víctor
author_role author
author2 Hernández Callejo, Luis
Duque Pérez, Óscar
Alonso Gómez, Víctor
author2_role author
author
author
dc.subject.none.fl_str_mv Wind turbines
Artificial intelligence
Inteligencia artificial
Motores de inducción
Faults diagnostic
Synthetic data
3313.30 Turbinas
topic Wind turbines
Artificial intelligence
Inteligencia artificial
Motores de inducción
Faults diagnostic
Synthetic data
3313.30 Turbinas
description Producción Científica
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/app11156942
https://uvadoc.uva.es/handle/10324/59537
url https://doi.org/10.3390/app11156942
https://uvadoc.uva.es/handle/10324/59537
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2076-3417/11/15/6942
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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