Machine learning methods for wind turbine condition monitoring: A review

Adrian Stetco, Fateme Dinmohammadi, Xingyu Zhao, Valentin Robu, David Flynn, Mike Barnes, John Keane and Goran Nenadic

Renewable Energy, 2019, vol. 133, issue C, 620-635

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.

Keywords: Renewable energy; Wind farms; Condition monitoring; Machine learning; Prognostic maintenance (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (86)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:133:y:2019:i:c:p:620-635

DOI: 10.1016/j.renene.2018.10.047

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