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Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates

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  • Murcia, Juan Pablo
  • Réthoré, Pierre-Elouan
  • Dimitrov, Nikolay
  • Natarajan, Anand
  • Sørensen, John Dalsgaard
  • Graf, Peter
  • Kim, Taeseong

Abstract

Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertainty models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. The surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.

Suggested Citation

  • Murcia, Juan Pablo & Réthoré, Pierre-Elouan & Dimitrov, Nikolay & Natarajan, Anand & Sørensen, John Dalsgaard & Graf, Peter & Kim, Taeseong, 2018. "Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates," Renewable Energy, Elsevier, vol. 119(C), pages 910-922.
  • Handle: RePEc:eee:renene:v:119:y:2018:i:c:p:910-922
    DOI: 10.1016/j.renene.2017.07.070
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    References listed on IDEAS

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    1. Abdallah, I. & Natarajan, A. & Sørensen, J.D., 2016. "Influence of the control system on wind turbine loads during power production in extreme turbulence: Structural reliability," Renewable Energy, Elsevier, vol. 87(P1), pages 464-477.
    2. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
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    Cited by:

    1. Jannie Sønderkær Nielsen & Lindsay Miller-Branovacki & Rupp Carriveau, 2021. "Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components," Energies, MDPI, vol. 14(4), pages 1-16, February.
    2. Thapa, Mishal & Missoum, Samy, 2022. "Uncertainty quantification and global sensitivity analysis of composite wind turbine blades," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Li, Xuan & Zhang, Wei, 2020. "Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions," Renewable Energy, Elsevier, vol. 159(C), pages 570-584.
    4. Hübler, Clemens, 2020. "Global sensitivity analysis for medium-dimensional structural engineering problems using stochastic collocation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Wilkie, David & Galasso, Carmine, 2020. "Impact of climate-change scenarios on offshore wind turbine structural performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    6. Shao, Yizhe & Liu, Jie, 2024. "Uncertainty quantification for dynamic responses of offshore wind turbine based on manifold learning," Renewable Energy, Elsevier, vol. 222(C).
    7. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    8. Velarde, Joey & Kramhøft, Claus & Sørensen, John Dalsgaard, 2019. "Global sensitivity analysis of offshore wind turbine foundation fatigue loads," Renewable Energy, Elsevier, vol. 140(C), pages 177-189.
    9. Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).

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