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Machine learning methods for wind turbine condition monitoring : a review
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Stetco, Adrian, Dinmohammadi, Fateme, Zhao, Xingyu, Robu, Valentin, Flynn, David, Barnes, Mike, Keane, John and Nenadic, Goran (2019) Machine learning methods for wind turbine condition monitoring : a review. Renewable Energy, 133 . pp. 620-635. doi:10.1016/j.renene.2018.10.047 ISSN 0960-1481.
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Official URL: http://dx.doi.org/10.1016/j.renene.2018.10.047
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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Journal or Publication Title: | Renewable Energy | ||||||||
Publisher: | Elsevier Ltd. | ||||||||
ISSN: | 0960-1481 | ||||||||
Official Date: | April 2019 | ||||||||
Dates: |
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Volume: | 133 | ||||||||
Page Range: | pp. 620-635 | ||||||||
DOI: | 10.1016/j.renene.2018.10.047 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) |
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