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Machine learning methods for wind turbine condition monitoring: A review

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. Green open access

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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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