Operation and maintenance optimization of offshore wind farms based on digital twin: A review

Xia, Jiajun; Zou, Guang

2023-01-15

10.1016/j.oceaneng.2022.113322

OCEAN ENGINEERING   影响因子和分区

0029-8018

1873-5258

© 2022 Elsevier Ltd

Offshore wind farms Digital twin Operation and maintenance Condition monitoring Value of information

EI ; SCI

英语

第一 ; 通讯

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.

Engineering ; Oceanography

Engineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography

WOS:000992355300001

Elsevier Ltd

20225113269943

Condition based maintenance ; Condition monitoring ; Costs ; Electric utilities ; Life cycle ; Offshore oil well production

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

ENGINEERING

EV Compendex

Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Xia, Jiajun,Zou, Guang. Operation and maintenance optimization of offshore wind farms based on digital twin: A review[J]. OCEAN ENGINEERING,2023,268.

Xia, Jiajun,&Zou, Guang.(2023).Operation and maintenance optimization of offshore wind farms based on digital twin: A review.OCEAN ENGINEERING,268.

Xia, Jiajun,et al."Operation and maintenance optimization of offshore wind farms based on digital twin: A review".OCEAN ENGINEERING 268(2023).