Machine learning methods for wind turbine condition monitoring: A review. (April 2019)

• Record Type: Journal Article
• Authors: Stetco, Adrian; Dinmohammadi, Fateme; Zhao, Xingyu; Robu, Valentin; Flynn, David; Barnes, Mike; Keane, John; Nenadic, Goran
• Is Part Of: Renewable energy. Volume 133(2019)
• Page Start: 620
• Page End: 635
• Publication Date: 2019-04
• Subjects: Renewable energy -- Wind farms -- Condition monitoring -- Machine learning -- Prognostic maintenance; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.10.047
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML steps, including data sources, feature selection and extraction, model selection (classification, regression), validation and decision-making. Our findings show that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression. Neural networks, support vector machines and decision trees are most commonly used. We conclude with a discussion of the main areas for future work in this domain. Highlights: Recent literature on machine learning models proposed for condition monitoring in wind turbines is reviewed . Models grouped by ML steps: data sources, feature selection/extraction, model selection, validation, decision making. Review focuses on various tasks including blade fault detection, generator temperature and power curve monitoring, etc. Findings show that models use SCADA or simulated data; around 2/3rds use classification, the rest rely on regression.