A fault detection framework using recurrent neural networks for condition monitoring of wind turbines-Reference-Cited by-同舟云学术

A fault detection framework using recurrent neural networks for condition monitoring of wind turbines

Published:2021-02-25 Issue:11 Volume:24 Page:1249-1262
ISSN:1095-4244
Container-title:Wind Energy
language:en
Short-container-title:Wind Energy

Author:

Cui Yue¹^ORCID,Bangalore Pramod²,Bertling Tjernberg Lina¹

Affiliation:

1. School of Electrical Engineering and Computer Engineering KTH Royal Institute of Technology Stockholm Sweden

2. Research Greenbyte AB Gothenburg Sweden

Funder

China Scholarship Council

Publisher

Wiley

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/we.2628

Reference36 articles.

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2. IRENA.Renewable power generation costs in 2017. tech. rep. Abu Dhabi International Renewable Energy Agency;2018.

3. Infrastructure Asset Management with Power System Applications

4. Wind Turbine Condition Monitoring Based on an Improved Spline-Kernelled Chirplet Transform

5. A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems

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