期刊
RENEWABLE ENERGY
卷 133, 期 -, 页码 620-635出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2018.10.047
关键词
Renewable energy; Wind farms; Condition monitoring; Machine learning; Prognostic maintenance
资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/P009743/1, EP/1.021463/1]
- EPSRC [EP/R026173/1, EP/P009743/1, EP/P001173/1] Funding Source: UKRI
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. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licensesiby/4.0/).
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