Machine Learning Methods for Wind Turbine Condition Monitoring: A Review

Research output: Contribution to journalArticle

  • External authors:
  • Adrian Stetco
  • Fateme Dinmohammadi
  • Xingyu Zhao
  • Valentin Robu
  • David Flynn
  • John Keane

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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)620-635
Number of pages15
JournalRenewable Energy
Volume133
Early online date9 Oct 2018
DOIs
Publication statusPublished - Apr 2019