A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques

This document examines the most up-to-date research on the application of machine learning (ML) techniques in monitoring the conditions of wind turbines. The focus is on classification methods, which are used to identify different types of faults. The analysis revealed that the majority of the resea...

Full description

Saved in:
Bibliographic Details
Published inE3S web of conferences Vol. 540; p. 3003
Main Authors Muralidharan, P., Thakur, Gaurav, M, Shalini, Sharma, Vikalp, Abootharmahmoodshakir, Dhablia, Anishkumar
Format Journal Article
LanguageEnglish
Published EDP Sciences 2024
Subjects
condition monitoring
machine learning
renewable energy
wind turbine
Online AccessGet full text

Cover

More Information
Summary:This document examines the most up-to-date research on the application of machine learning (ML) techniques in monitoring the conditions of wind turbines. The focus is on classification methods, which are used to identify different types of faults. The analysis revealed that the majority of the research utilizes Supervisory Control and Data Acquisition (SCADA) information, with neural networks, support vector machines, and decision trees being the most prevalent machine learning algorithms. The review also identifies several areas for future research, such as the development of more robust ML models that can handle noisy data and the use of ML methods for prognosis (predicting future faults).
ISSN:2267-1242
2267-1242
DOI:10.1051/e3sconf/202454003003