Stetco, A;
Dinmohammadi, F;
Zhao, X;
Robu, V;
Flynn, D;
Barnes, M;
Keane, J;
(2019)
Machine learning methods for wind turbine condition monitoring: A review.
Renewable Energy
, 133
pp. 620-635.
10.1016/j.renene.2018.10.047.
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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.
| Type: | Article |
|---|---|
| Title: | Machine learning methods for wind turbine condition monitoring: A review |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.renene.2018.10.047 |
| Publisher version: | https://doi.org/10.1016/j.renene.2018.10.047 |
| Language: | English |
| Additional information: | Copyright © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Renewable energy, Wind farms, Condition monitoring, Machine learning, Prognostic maintenance |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10107608 |
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