Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review

With the increase in the installed capacity of wind power systems, the fault diagnosis and condition monitoring of wind turbines (WT) has attracted increasing attention. In recent years, machine learning (ML) has played a crucial role as an emerging technology for fault diagnosis in wind power syste...

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Autores principales:	Tongda Sun, Gang Yu, Mang Gao, Lulu Zhao, Chen Bai, Wanqian Yang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Wind turbines machine learning fault diagnosis review Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/59c3f5f76d834ed6bfbfac899376b285
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spelling	oai:doaj.org-article:59c3f5f76d834ed6bfbfac899376b2852021-11-18T00:10:56ZFault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review2169-353610.1109/ACCESS.2021.3124025https://doaj.org/article/59c3f5f76d834ed6bfbfac899376b2852021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592795/https://doaj.org/toc/2169-3536With the increase in the installed capacity of wind power systems, the fault diagnosis and condition monitoring of wind turbines (WT) has attracted increasing attention. In recent years, machine learning (ML) has played a crucial role as an emerging technology for fault diagnosis in wind power systems has played a crucial role. Even though ML methods have shown great potential in dealing with the issues related to the fault diagnosis of WT, there are still some challenges encountered in many aspects. In this paper, typical fault diagnosis methods based on ML methods for wind power systems are thoroughly reviewed in terms of both theoretical fundamentals and industrial applications, including traditional machine learning (TML), artificial neural networks (ANN), deep learning (DL) and transfer learning (TL), in the development line of ML technologies. The advantages and disadvantages of various methods are analyzed and discussed. Meanwhile, a distribution diagram is provided for the discussions of ML methods applied for WT fault diagnosis, and the existing challenges on the applications for fault diagnosis based on ML for wind power generation systems are presented. Moreover, some prospects for future research directions are provided.Tongda SunGang YuMang GaoLulu ZhaoChen BaiWanqian YangIEEEarticleWind turbinesmachine learningfault diagnosisreviewElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147481-147511 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wind turbines machine learning fault diagnosis review Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Wind turbines machine learning fault diagnosis review Electrical engineering. Electronics. Nuclear engineering TK1-9971 Tongda Sun Gang Yu Mang Gao Lulu Zhao Chen Bai Wanqian Yang Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review
description	With the increase in the installed capacity of wind power systems, the fault diagnosis and condition monitoring of wind turbines (WT) has attracted increasing attention. In recent years, machine learning (ML) has played a crucial role as an emerging technology for fault diagnosis in wind power systems has played a crucial role. Even though ML methods have shown great potential in dealing with the issues related to the fault diagnosis of WT, there are still some challenges encountered in many aspects. In this paper, typical fault diagnosis methods based on ML methods for wind power systems are thoroughly reviewed in terms of both theoretical fundamentals and industrial applications, including traditional machine learning (TML), artificial neural networks (ANN), deep learning (DL) and transfer learning (TL), in the development line of ML technologies. The advantages and disadvantages of various methods are analyzed and discussed. Meanwhile, a distribution diagram is provided for the discussions of ML methods applied for WT fault diagnosis, and the existing challenges on the applications for fault diagnosis based on ML for wind power generation systems are presented. Moreover, some prospects for future research directions are provided.
format	article
author	Tongda Sun Gang Yu Mang Gao Lulu Zhao Chen Bai Wanqian Yang
author_facet	Tongda Sun Gang Yu Mang Gao Lulu Zhao Chen Bai Wanqian Yang
author_sort	Tongda Sun
title	Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review
title_short	Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review
title_full	Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review
title_fullStr	Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review
title_full_unstemmed	Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review
title_sort	fault diagnosis methods based on machine learning and its applications for wind turbines: a review
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/59c3f5f76d834ed6bfbfac899376b285
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Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review

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