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Surrogate model uncertainty in wind turbine reliability assessment

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  • Slot, René M.M.
  • Sørensen, John D.
  • Sudret, Bruno
  • Svenningsen, Lasse
  • Thøgersen, Morten L.

Abstract

Lowering the cost of wind energy entails the optimization of wind turbine material consumption without compromising structural safety. Traditionally, wind turbines are designed by the partial safety factor method, which is calibrated by probabilistic models and presented in the IEC 61400-1 design standard. This approach significantly reduces the amount of aero-elastic simulations required to assess the fatigue limit state of wind turbines, but it may lead to inconsistent reliability levels across wind farm projects. To avoid this, wind turbines may be designed by probabilistic methods using surrogate models to approximate fatigue load effects. In this approach, it is important to quantify and model all relevant uncertainties including that of the surrogate model itself. Here we quantify this uncertainty according to Eurocode 1990 for polynomial chaos expansion (PCE) and Kriging using wind data from 99 real sites and the 5 MW reference turbine designed by NREL. We investigate a wide range of simulation efforts to train the surrogate models. Our results show that Kriging yields a higher accuracy per invested simulation compared to PCE. This improved understanding of utilizing PCE and Kriging in fatigue reliability assessment may significantly benefit decision support in probabilistic design of wind turbines.

Suggested Citation

  • Slot, René M.M. & Sørensen, John D. & Sudret, Bruno & Svenningsen, Lasse & Thøgersen, Morten L., 2020. "Surrogate model uncertainty in wind turbine reliability assessment," Renewable Energy, Elsevier, vol. 151(C), pages 1150-1162.
    • Handle: RePEc:eee:renene:v:151:y:2020:i:c:p:1150-1162
    DOI: 10.1016/j.renene.2019.11.101
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    References listed on IDEAS

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    1. Toft, Henrik Stensgaard & Svenningsen, Lasse & Sørensen, John Dalsgaard & Moser, Wolfgang & Thøgersen, Morten Lybech, 2016. "Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads," Renewable Energy, Elsevier, vol. 90(C), pages 352-361.
    2. Andrianakis, Ioannis & Challenor, Peter G., 2012. "The effect of the nugget on Gaussian process emulators of computer models," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4215-4228.
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    2. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    3. Postnikov, Ivan, 2022. "A reliability assessment of the heating from a hybrid energy source based on combined heat and power and wind power plants," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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    6. Samantha M. Roth & Ben Seiyon Lee & Sanjib Sharma & Iman Hosseini‐Shakib & Klaus Keller & Murali Haran, 2023. "Flood hazard model calibration using multiresolution model output," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    7. Mohammad Hassan Ranjbar & Behnam Rafiei & Seyyed Abolfazl Nasrazadani & Kobra Gharali & Madjid Soltani & Armughan Al-Haq & Jatin Nathwani, 2021. "Power Enhancement of a Vertical Axis Wind Turbine Equipped with an Improved Duct," Energies, MDPI, vol. 14(18), pages 1-16, September.
    8. Jannie Sønderkær Nielsen & Henrik Stensgaard Toft & Gustavo Oliveira Violato, 2023. "Risk-Based Assessment of the Reliability Level for Extreme Limit States in IEC 61400-1," Energies, MDPI, vol. 16(4), pages 1-15, February.
    9. Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.
    10. Zhang, Xiaoling & Zhang, Kejia & Yang, Xiao & Fazeres-Ferradosa, Tiago & Zhu, Shun-Peng, 2023. "Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling," Renewable Energy, Elsevier, vol. 206(C), pages 552-565.

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