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Empowering digital twins for wind energy operation and maintenance: A prospective framework and future directions
Li, M.; Kikuchi, Y.; Ringsberg, J.W.; Gryllias, K.C.; Baraldi, P.; Zio, E.; Carroll, J. (2026). Empowering digital twins for wind energy operation and maintenance: A prospective framework and future directions. Renew. Sust. Energ. Rev. 235: 116970. https://dx.doi.org/10.1016/j.rser.2026.116970
In: Renewable & Sustainable Energy Reviews. Elsevier/Elsevier Science: Oxford. ISSN 1364-0321; e-ISSN 1879-0690, meer
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| Author keywords |
Wind energy;Offshore wind;Digital twin;Prognostics and health management;Operation and maintenance;Artificial intelligence;of-the-art;fault-diagnosis;failure mode;turbine;reliability;cost;uncertainty;gearbox;systems;technologies |
| Auteurs | | Top |
- Li, M.
- Kikuchi, Y.
- Ringsberg, J.W.
- Gryllias, K.C., meer
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- Baraldi, P.
- Zio, E.
- Carroll, J.
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| Abstract |
Wind energy is a cornerstone in the global transition toward carbon neutrality, with its long Operation and Maintenance (O&M) phase playing a significant role in affecting overall profitability, efficiency, safety, and sus tainability. Digital twin (DT) technology has emerged as a key frontier in the wind energy sector due to its potential to construct comprehensive virtual representations of physical wind turbines and enable a digitalized loop to enhance performance across the entire life cycle. While research on DT technology in wind energy O&M is rapidly gaining visibility, there remains a substantial gap between current academic developments and practi cal implementation across methodological, technical, and operational aspects. In order to address this issue, this paper begins by briefly summarizing recent trends in wind turbine technology and identifying the most critical components that deserve DT technology. The existing DT capability levels and modeling approaches are then reviewed, and a prospective DT framework specifically tailored for the O&M of wind energy systems is proposed. The proposed framework encompasses a closed-loop process from the physical to the virtual domain and back again. The physical-to-virtual loop includes data acquisition, data management, virtual model construction, and adaptive operations. Conversely, the virtual-to-physical loop involves diagnostics and prognostics, maintenance decision-making, resource planning, and maintenance execution. Each stage is analyzed in terms of its enabling technologies and representative methodologies. By comparing the state of the art with the envisioned DT-enabled O&M paradigm, this paper identifies key research gaps and outlines promising directions for future investigation. |
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