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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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.147481-147511
Main Authors: Sun, Tongda, Yu, Gang, Gao, Mang, Zhao, Lulu, Bai, Chen, Yang, Wanqian
Format: Article
Language:English
Subjects:
Artificial neural networks
Condition monitoring
Deep learning
Fault diagnosis
Industrial applications
Machine learning
Neurons
New technology
Optimization
review
Support vector machines
Training
Transfer learning
Wind power
Wind power generation
Wind turbines
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3124025