Open Access
Issue
E3S Web Conf.
Volume 546, 2024
2024 2nd International Conference on Green Building (ICoGB 2024)
Article Number 02010
Number of page(s) 9
Section Green Building Technology and Innovation
DOI https://doi.org/10.1051/e3sconf/202454602010
Published online 09 July 2024
  1. J. Lee, F. Zhao, Global Wind Report 2022, (Global Wind Energy Council, 1000 Brussels, 2022) [Google Scholar]
  2. C. Yang, Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey. Energies. 2023, 16, 5562. (2023) [CrossRef] [Google Scholar]
  3. Y. Wang. Research on digital twin technology of large-scale offshore wind power flexible direct transmission system. J. Phys.: Conf. Ser. 2450, 012046. (2023) [CrossRef] [Google Scholar]
  4. J. Walker, Digital twins of the mooring line tension for floating offshore wind turbines to improve monitoring, lifespan, and safety. J. Ocean Eng. Mar. Energy. 8, 1–16. (2021) [Google Scholar]
  5. M. Wang, Recent progress on reliability analysis of offshore wind turbine support structures considering digital twin solutions. Ocean. Eng. 232, 109168. (2021) [CrossRef] [Google Scholar]
  6. A. Haghshenas, Predictive digital twin for offshore wind farms. Energy. Inform. 6, 1–26. (2023) [CrossRef] [Google Scholar]
  7. J. Jorgensen, Requirements for the application of the Digital Twin Paradigm to offshore wind turbine structures for uncertain fatigue analysis. Comput. Ind. 145, 103086. (2023) [Google Scholar]
  8. D. Augustyn, Reliability Updating of Offshore Wind Substructures by Use of Digital Twin Information. Energies. 14, 5859. (2021) [CrossRef] [Google Scholar]
  9. M. W. Grieves Digital twin: manufacturing excellence through virtual factory replication. White paper. 1 (2014), 1–7. (2015) [Google Scholar]
  10. E. Glaessgen, D. Stargel, The digital twin paradigm for future NASA and US Air Force vehicles. in Proceedings of 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Honolulu, Hawaii, USA, April 23-26, (2012), 1818 [Google Scholar]
  11. T. Fei, Five-dimension digital twin model and its ten applications. CIMS, 25(1), 1–18. (2019) [Google Scholar]
  12. M. W. Grieves, J. Vickers, Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems, (Transdisciplinary Perspectives on Complex Systems, Springer, 2017) [Google Scholar]
  13. T. Fei, M. Zhang, Digital twin driven prognostics and health management for complex equipment. Cirp. Ann-Manuf. Techn. 67, 169–172. (2018) [CrossRef] [Google Scholar]
  14. J. Dai, Effect investigation of yaw on wind turbine performance based on SCADA data. Energy, 149, 684–696. (2018) [CrossRef] [Google Scholar]
  15. L.J. Vermeer, Wind turbine wake aerodynamics. Prog. Aerosp. Sci. 39(6-7), 467–510. (2003). [CrossRef] [Google Scholar]
  16. R. Nash, Wind turbine wake control strategies: A review and concept proposal. Energy Convers. Manage. 245, 114581. (2021) [CrossRef] [Google Scholar]
  17. B. Yu, A review of the application of oil analysis in condition monitoring and life prediction of wind turbine gearboxes. Insight. 63(5), 289–301. (2021) [CrossRef] [Google Scholar]
  18. Y. Zhang, Planet gear fault localization for wind turbine gearbox using acoustic emission signals. Renew. Energy. 109, 449–460. (2017) [CrossRef] [Google Scholar]

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