A Review of machine learning techniques for wind turbine’s fault detection, diagnosis, and prognosis-Reference-Cited by-同舟云学术

A Review of machine learning techniques for wind turbine’s fault detection, diagnosis, and prognosis

Published:2023-05-29 Issue:4 Volume:21 Page:771-786
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Khan Prince Waqas¹,Byun Yung-Cheol²

Affiliation:

1. School of Computing, Gachon University, Seongnam-Si, Gyeonggi-Do, South Korea

2. Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science Technology, Jeju National University, Jeju, South Korea

Funder

Korea Technology and Information Promotion Agency for SME

Publisher

Informa UK Limited

Subject

Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2023.2217901

Reference104 articles.

1. Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data

2. Abid, K., M. Sayed Mouchaweh, and L. Cornez. 2018. “Fault prognostics for the predictive maintenance of wind turbines: state of the art.” In Joint european conference on machine learning and knowledge discovery in databases, Dublin, Ireland, 113–25. Springer.

3. Non-polluting power supply solutions for homes;Adina T.;Annals Of’constantin Brancusi’university of Targu-Jiu: Engineering Series (4),2018

4. Machine learning-based wind speed time series analysis

5. Vibration based fault diagnostics in a wind turbine planetary gearbox using machine learning

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