dc.contributor
Universitat Politècnica de Catalunya. Doctorat en Enginyeria Sísmica i Dinàmica Estructural
dc.contributor
Universitat Politècnica de Catalunya. Departament de Matemàtiques
dc.contributor
Universitat Politècnica de Catalunya. CoDAlab - Control, Dades i Intel·ligència Artificial
dc.contributor.author
Wang, Shun
dc.contributor.author
Vidal Seguí, Yolanda
dc.contributor.author
Pozo Montero, Francesc
dc.date.accessioned
2026-03-03T00:57:56Z
dc.date.available
2026-03-03T00:57:56Z
dc.date.issued
2025-10-29
dc.identifier
Wang, S.; Vidal, Y.; Pozo, F. Recent advances in wind turbine condition monitoring using SCADA data: a state-of-the-art review. «Reliability engineering & systems safety», 29 Octubre 2025, vol. 267, part A, núm. article 111838.
dc.identifier
https://hdl.handle.net/2117/456230
dc.identifier
10.1016/j.ress.2025.111838
dc.identifier.uri
https://hdl.handle.net/2117/456230
dc.description.abstract
With the rapid increase worldwide in the number of installed wind turbines, as an increasingly vital role in the global energy landscape, requirements and expenses of maintenance have also increased significantly. This surge in wind turbine installations worldwide has escalated maintenance demands and costs, making condition monitoring an essential focus for research and operational optimization. Built-in supervisory control and data acquisition (SCADA) systems could offer a cost-effective approach to condition monitoring in wind turbines, using existing infrastructure to reduce the need for additional sensors and simplifying system complexity. This review critically surveys state-of-the-art SCADA-based condition monitoring techniques for wind turbines. The paper first examines essential data preprocessing strategies tailored for SCADA data challenges. Subsequently, key monitoring methodologies are reviewed, including model-based and data-driven paradigms, analyzing their applications, strengths, and limitations. Finally, the challenges and future trends of SCADA-based wind turbine condition monitoring are discussed. This comprehensive overview serves as a valuable reference for researchers and industry practitioners seeking to enhance wind turbine reliability and operational efficiency through advanced monitoring techniques.
dc.description.abstract
This work is partially funded by the Spanish Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER), Spain through the research projects PID2021-122132OB-C21 and TED2021-129512B-I00, and by the Generalitat de Catalunya, Spain through the research project 2021-SGR-01044.
dc.description.abstract
7 - Energia Assequible i No Contaminant
dc.description.abstract
7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el conjunt de fonts d’energia
dc.description.abstract
7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energètica
dc.description.abstract
8 - Treball Decent i Creixement Econòmic
dc.description.abstract
11 - Ciutats i Comunitats Sostenibles
dc.description.abstract
13 - Acció per al Clima
dc.description.abstract
Postprint (published version)
dc.format
application/pdf
dc.relation
https://www.sciencedirect.com/science/article/pii/S0951832025010385
dc.relation
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122132OB-C21/ES/DESARROLLO Y VALIDACION DE ESTRATEGIAS DE APRENDIZAJE PROFUNDO Y AUTOMATICO PARA EL MANTENIMIENTO PREDICTIVO Y DETECCION TEMPRANA DE DAÑOS ESTRUCTURALES EN AEROGENERADORES/
dc.relation
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129512B-I00/ES/Gemelos digitales para la monitorización de la condición de aerogeneradores/
dc.rights
http://creativecommons.org/licenses/by/4.0/
dc.rights
Attribution 4.0 International
dc.subject
Àrees temàtiques de la UPC::Matemàtiques i estadística
dc.subject
Condition monitoring
dc.subject
Fault detection
dc.subject
Fault diagnosis
dc.subject
Fault prognosis
dc.title
Recent advances in wind turbine condition monitoring using SCADA data: a state-of-the-art review
