| Title | Operation and maintenance optimization of offshore wind farms based on digital twin: A review |
| Author | |
| Corresponding Author | Zou, Guang |
| Publication Years | 2023-01-15
|
| DOI | |
| Source Title | |
| ISSN | 0029-8018
|
| Volume | 268 |
| Abstract | As one of the most promising clean energy sources, offshore wind farms (OWFs) have developed rapidly in countries around the world. However, due to complex weather and geological environments and increasing distance from the shore, operations and maintenance (O&M) costs of OWFs are much higher than those of other clean energy sources, accounting for as much as 30% of total life-cycle costs. Issues such as OWFs system reliability, O&M operator safety and ecological protection issues also become more and more prominent. To address these challenges, this paper reviews the latest research progress on digital twin (DT) technology targeting on OWFs O&M, including failure analysis, O&M objectives, strategies & optimization models, DT technology development, as well as DT-based O&M management and optimization. A DT-based O&M optimization framework is proposed which helps to improve the intelligence level of O&M. Computation of the value of a DT model is discussed and value-informed DT model development is proposed. Promising research areas on O&M optimization are identified. © 2022 Elsevier Ltd |
| Indexed By | |
| Language | English
|
| SUSTech Authorship | First
; Corresponding
|
| Funding Project | Recently, DT technology is also found application in the intelligent O&M decision-making of OWFs (Errandonea et al., 2020). Sivalingam et al. (2018) use a physics-based approach to predict the remaining life of an offshore wind turbine and construct an O&M DT platform in support of developing to optimal the predictive maintenance strategies of OWFs. Werner et al. (2019) use DT to achieve preventive maintenance strategy decision-making, according to the decision results, the maintenance efficiency and costs are enhanced. Ma et al. (2020) propose a data-driven DT model for decision-making on equipment maintenance by integrating three technologies, e.g., reliability-centered maintenance, BIM and GIS. BIM and GIS are integrated to support the acquisition and update of data required for maintenance. Besides, this DT model can provide a virtual environment for maintenance path planning. Mi et al. (2021) propose an interconnection framework across multiple organization for total factors influencing maintenance decision-making, as a key supporting technology, DT is integrated into it to improve the accuracy of failure prediction and make a maintenance plan with higher accuracy and reliability. Van Dinter et al. (2022) review current research on DT technology in predictive maintenance, and point out that data processing burden, data diversity and complexity of models are the main challenges of applying DT technology in the design of a predictive maintenance strategy. In the future, the DT-based O&M decision-making of OWFs should be based on comprehensive data and indicators, and machine learning techniques can also be used to improve the accuracy of decision-making.The funding support from the Southern University of Science and Technology (No. Y01316134 to Guang Zou) is greatly appreciated.The funding support from the Southern University of Science and Technology (No. Y01316134 to Guang Zou) is greatly appreciated.
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| Publisher | |
| EI Accession Number | 20225113269943
|
| EI Keywords | Condition based maintenance
; Condition monitoring
; Costs
; Electric utilities
; Life cycle
; Offshore oil well production
|
| ESI Classification Code | Oil Field Production Operations:511.1
; Wind Power (Before 1993, use code 611 ):615.8
; Cost and Value Engineering; Industrial Economics:911
; Maintenance:913.5
|
| ESI Research Field | ENGINEERING
|
| Data Source | EV Compendex
|
| Citation statistics |
Cited Times [WOS]:4
|
| Document Type | Journal Article |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/519649 |
| Department | Department of Ocean Science and Engineering |
| Affiliation | Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China |
| First Author Affilication | Department of Ocean Science and Engineering |
| Corresponding Author Affilication | Department of Ocean Science and Engineering |
| First Author's First Affilication | Department of Ocean Science and Engineering |
| Recommended Citation GB/T 7714 |
Xia, Jiajun,Zou, Guang. Operation and maintenance optimization of offshore wind farms based on digital twin: A review[J]. OCEAN ENGINEERING,2023,268.
|
| APA |
Xia, Jiajun,&Zou, Guang.(2023).Operation and maintenance optimization of offshore wind farms based on digital twin: A review.OCEAN ENGINEERING,268.
|
| MLA |
Xia, Jiajun,et al."Operation and maintenance optimization of offshore wind farms based on digital twin: A review".OCEAN ENGINEERING 268(2023).
|
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