Universitat Politècnica de Catalunya. Doctorat en Enginyeria Sísmica i Dinàmica Estructural
Universitat Politècnica de Catalunya. Departament de Matemàtiques
Universitat Politècnica de Catalunya. CoDAlab - Control, Dades i Intel·ligència Artificial
2025-10-29
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.
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.
7 - Energia Assequible i No Contaminant
7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el conjunt de fonts d’energia
7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energètica
8 - Treball Decent i Creixement Econòmic
11 - Ciutats i Comunitats Sostenibles
13 - Acció per al Clima
Postprint (published version)
Article
Anglès
Àrees temàtiques de la UPC::Matemàtiques i estadística; Wind energy; Wind turbine; SCADA; Condition monitoring; Fault detection; Fault diagnosis; Fault prognosis
Elsevier
https://www.sciencedirect.com/science/article/pii/S0951832025010385
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/
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/
http://creativecommons.org/licenses/by/4.0/
Open Access
Attribution 4.0 International
E-prints [72263]