Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can...

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Detalles Bibliográficos
Autores: Maldonado Correa, Jorge, Martín Martínez, Sergio, Artigao Andicoberry, Estefanía, Gómez Lázaro, Emilio
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/41261
Acceso en línea:http://10.3390/en13123132
https://hdl.handle.net/10578/41261
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Condition monitoring
Fault prediction
SCADA data
Wind turbine
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spelling Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature ReviewMaldonado Correa, JorgeMartín Martínez, SergioArtigao Andicoberry, EstefaníaGómez Lázaro, EmilioArtificial intelligenceCondition monitoringFault predictionSCADA dataWind turbineOperation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an eective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.MDPI202520252020info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://10.3390/en13123132https://hdl.handle.net/10578/41261reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésENE2016-78214-C2-1-Rinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/412612026-05-27T07:36:41Z
dc.title.none.fl_str_mv Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
title Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
spellingShingle Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
Maldonado Correa, Jorge
Artificial intelligence
Condition monitoring
Fault prediction
SCADA data
Wind turbine
title_short Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
title_full Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
title_fullStr Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
title_full_unstemmed Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
title_sort Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
dc.creator.none.fl_str_mv Maldonado Correa, Jorge
Martín Martínez, Sergio
Artigao Andicoberry, Estefanía
Gómez Lázaro, Emilio
author Maldonado Correa, Jorge
author_facet Maldonado Correa, Jorge
Martín Martínez, Sergio
Artigao Andicoberry, Estefanía
Gómez Lázaro, Emilio
author_role author
author2 Martín Martínez, Sergio
Artigao Andicoberry, Estefanía
Gómez Lázaro, Emilio
author2_role author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Condition monitoring
Fault prediction
SCADA data
Wind turbine
topic Artificial intelligence
Condition monitoring
Fault prediction
SCADA data
Wind turbine
description Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an eective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.
publishDate 2020
dc.date.none.fl_str_mv 2020
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://10.3390/en13123132
https://hdl.handle.net/10578/41261
url http://10.3390/en13123132
https://hdl.handle.net/10578/41261
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv ENE2016-78214-C2-1-R
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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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