Wang, G., Wei, H.-L. orcid.org/0000-0002-4704-7346 and Liu, Z.-H. (2024) An intelligent state evaluation and maintenance arrangement system for wind turbines based on digital twin. Academia Engineering, 1 (4). ISSN 2994-7065
Abstract
Wind power is an important green and sustainable source of power generation. However, the construction of wind farms does not only need a large amount of initial investment but also highly expensive maintenance cost for their operations during power generation. Therefore, accurately assessing the state of wind turbines and effectively scheduling maintenance to keep them in good operating condition have become crucially important to ensure efficient power generation. Digital twin, as a data-driven digital concept or technology, can be used to effectively address wind power maintenance issues, especially wind turbine state evaluation problem. This article proposes a novel intelligent state evaluation and maintenance arrangement (iSEMA) system based on digital twin, which can accurately evaluate the state of wind turbines, detect faults in the early stage, and provide useful information or warnings to operators and help them to efficiently arrange maintenance tasks. In addition, this article introduces the concept of sub-healthy state of wind turbines, which is very useful for designing the iSEMA system. Experimental results demonstrate that the proposed system can assess the state of wind turbines accurately and provide timely feedback.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 copyright by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. org/licenses/by/4.0/). |
Keywords: | wind turbine; state evaluation; maintenance arrangement; long short-term memory; digital twin |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Funding Information: | Funder Grant number ROYAL SOCIETY IEC\NSFC\223266 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/H00453X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Oct 2024 16:16 |
Last Modified: | 30 Oct 2024 16:16 |
Status: | Published |
Publisher: | Academia.edu Journals |
Refereed: | Yes |
Identification Number: | 10.20935/acadeng7391 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219017 |
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