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Digital twining of an offshore wind turbine on a monopile using reduced-order modelling approach

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  • Zhao, Xiang
  • Dao, My Ha
  • Le, Quang Tuyen

Abstract

In the wind energy industry, a cost-benefit digital twin (DT) will be very useful for managing the operation of a wind turbine. For instance, a DT could provide information on structural health conditions in real-time as well as projections for the near future. In this work, we employ a component-based Reduced-Order Modelling (ROM) technique to construct a DT of a parameter-varying offshore wind turbine system on a monopile. The DT consists of a ROM model for modal analysis and structural response prediction under wind and wave loadings. First, an offline library is pre-computed, which contains a series of component archetypes. Then, the component-based ROM is assembled out of these archetypes, such as the parts in the blade, hub, nacelle, and tower. With the computation speed of two orders (approximately 650x) faster than a Finite Element Analysis (FEA) model and high accuracy (less than 0.2% error), the DT could be able to provide almost instant predictions of the modes and the responses of the turbine structure due to the wind-wave loading as well as projections of structural health conditions.

Suggested Citation

  • Zhao, Xiang & Dao, My Ha & Le, Quang Tuyen, 2023. "Digital twining of an offshore wind turbine on a monopile using reduced-order modelling approach," Renewable Energy, Elsevier, vol. 206(C), pages 531-551.
    • Handle: RePEc:eee:renene:v:206:y:2023:i:c:p:531-551
    DOI: 10.1016/j.renene.2023.02.067
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    Cited by:

    1. Li, Jichao & Dao, My Ha & Le, Quang Tuyen, 2024. "Data-driven modal parameterization for robust aerodynamic shape optimization of wind turbine blades," Renewable Energy, Elsevier, vol. 224(C).
    2. Dao, My Ha & Le, Quang Tuyen & Zhao, Xiang & Ooi, Chin Chun & Duong, Luu Trung Pham & Raghavan, Nagarajan, 2024. "Modelling of aero-mechanical response of wind turbine blade with damages by computational fluid dynamics, finite element analysis and Bayesian network," Renewable Energy, Elsevier, vol. 227(C).
    3. Wang, Bingkai & Sun, Wenlei & Wang, Hongwei & Xu, Tiantian & Zou, Yi, 2024. "Research on rapid calculation method of wind turbine blade strain for digital twin," Renewable Energy, Elsevier, vol. 221(C).

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