Wind turbine fault detection based on the transformer model using SCADA data

The growth of installed wind power worldwide and its significant contribution to the energy market is mainly due to the evolution of wind turbines (WTs) and their ability to withstand a wide range of dynamic loads. WT failures can be costly and lead to extended downtime. Early detection of such fail...

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Authors: Maldonado Correa, Jorge, Torres Cabrera, Joel, Martín Martínez, Sergio, Artigao Andicoberry, Estefanía, Gómez Lázaro, Emilio
Format: article
Publication Date:2024
Country:España
Institution:Universidad de Castilla-La Mancha
Repository:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/43084
Online Access:https://hdl.handle.net/10578/43084
Access Level:Open access
Keyword:Wind turbines
Converter
SCADA data
Transformers model
Faults prediction
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oai_identifier_str oai:ruidera.uclm.es:10578/43084
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repository_id_str
spelling Wind turbine fault detection based on the transformer model using SCADA dataMaldonado Correa, JorgeTorres Cabrera, JoelMartín Martínez, SergioArtigao Andicoberry, EstefaníaGómez Lázaro, EmilioWind turbinesConverterSCADA dataTransformers modelFaults predictionThe growth of installed wind power worldwide and its significant contribution to the energy market is mainly due to the evolution of wind turbines (WTs) and their ability to withstand a wide range of dynamic loads. WT failures can be costly and lead to extended downtime. Early detection of such failures is critical in reducing the costs associated with operation and maintenance (O&M) tasks and unscheduled shutdowns of WTs. This paper applies two Deep Learning (DL) models based on the Transformer model to predict failures in the IGBT module of WTs at an onshore wind farm in Ecuador. To this end, SCADA (Supervisory Control and Data Acquisition) operational and alarm data are used, together with the maintenance record (MR). These data are analyzed and processed, applying different feature selection methods. The results show that the two proposed models perform well, with high accuracy and an approximate prediction of 4.25 months before failure occurrence. The results are promising due to the possibility of using SCADA data for early and accurate identification of faults in different components of WTs.Elsevier202520252024info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10578/43084reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésSBPLY/19/180501/000287info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/oai:ruidera.uclm.es:10578/430842026-05-27T07:36:41Z
dc.title.none.fl_str_mv Wind turbine fault detection based on the transformer model using SCADA data
title Wind turbine fault detection based on the transformer model using SCADA data
spellingShingle Wind turbine fault detection based on the transformer model using SCADA data
Maldonado Correa, Jorge
Wind turbines
Converter
SCADA data
Transformers model
Faults prediction
title_short Wind turbine fault detection based on the transformer model using SCADA data
title_full Wind turbine fault detection based on the transformer model using SCADA data
title_fullStr Wind turbine fault detection based on the transformer model using SCADA data
title_full_unstemmed Wind turbine fault detection based on the transformer model using SCADA data
title_sort Wind turbine fault detection based on the transformer model using SCADA data
dc.creator.none.fl_str_mv Maldonado Correa, Jorge
Torres Cabrera, Joel
Martín Martínez, Sergio
Artigao Andicoberry, Estefanía
Gómez Lázaro, Emilio
author Maldonado Correa, Jorge
author_facet Maldonado Correa, Jorge
Torres Cabrera, Joel
Martín Martínez, Sergio
Artigao Andicoberry, Estefanía
Gómez Lázaro, Emilio
author_role author
author2 Torres Cabrera, Joel
Martín Martínez, Sergio
Artigao Andicoberry, Estefanía
Gómez Lázaro, Emilio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Wind turbines
Converter
SCADA data
Transformers model
Faults prediction
topic Wind turbines
Converter
SCADA data
Transformers model
Faults prediction
description The growth of installed wind power worldwide and its significant contribution to the energy market is mainly due to the evolution of wind turbines (WTs) and their ability to withstand a wide range of dynamic loads. WT failures can be costly and lead to extended downtime. Early detection of such failures is critical in reducing the costs associated with operation and maintenance (O&M) tasks and unscheduled shutdowns of WTs. This paper applies two Deep Learning (DL) models based on the Transformer model to predict failures in the IGBT module of WTs at an onshore wind farm in Ecuador. To this end, SCADA (Supervisory Control and Data Acquisition) operational and alarm data are used, together with the maintenance record (MR). These data are analyzed and processed, applying different feature selection methods. The results show that the two proposed models perform well, with high accuracy and an approximate prediction of 4.25 months before failure occurrence. The results are promising due to the possibility of using SCADA data for early and accurate identification of faults in different components of WTs.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10578/43084
url https://hdl.handle.net/10578/43084
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv SBPLY/19/180501/000287
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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