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Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling

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  • Zhang, Xiaoling
  • Zhang, Kejia
  • Yang, Xiao
  • Fazeres-Ferradosa, Tiago
  • Zhu, Shun-Peng

Abstract

Reliability of blades has a profound impact on both serviceability and safety of offshore wind turbines. Reliability estimation of wind turbine blade is a complex and time-consuming problem with multi-physics coupling and multi-failure modes correlation. Developing physics-of-failure modeling and reliability analysis methods with high efficiency and accuracy is a long-term challenge. In this work, a high availability and cost-effectiveness reliability estimation framework for offshore wind turbine blade by combining transfer learning (TL) and direct probability integral method (DPIM) is proposed. Extensive performance simulation of offshore wind turbine blade is a complex and necessary task, this paper develops a new adaptive sampling strategy to improve the validity of sample selection in the design space; On this basis, a physics-of-failure surrogate modeling approach is proposed by introducing TL method to fuse two kinds of multi-physics coupling analysis data, then the performance of all critical loads can be predicted efficiently in advance to provide a reliable design; Further, this paper provides an efficient reliability estimation method for offshore wind turbine blades by combining DPIM and surrogate model. Finally, the validity of the proposed approach is illustrated by numerical example and offshore wind turbine blade reliability estimation. The proposed framework provides a cost-effective alternative to higher loads simulation efforts and safety factors selection.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:206:y:2023:i:c:p:552-565
    DOI: 10.1016/j.renene.2023.02.026
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    References listed on IDEAS

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    1. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    2. Fabian Vorpahl & Holger Schwarze & Tim Fischer & Marc Seidel & Jason Jonkman, 2013. "Offshore wind turbine environment, loads, simulation, and design," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 2(5), pages 548-570, September.
    3. Zhiyu Jiang & Weifei Hu & Wenbin Dong & Zhen Gao & Zhengru Ren, 2017. "Structural Reliability Analysis of Wind Turbines: A Review," Energies, MDPI, vol. 10(12), pages 1-25, December.
    4. Larwood, Scott & van Dam, C.P. & Schow, Daniel, 2014. "Design studies of swept wind turbine blades," Renewable Energy, Elsevier, vol. 71(C), pages 563-571.
    5. Vučina, Damir & Marinić-Kragić, Ivo & Milas, Zoran, 2016. "Numerical models for robust shape optimization of wind turbine blades," Renewable Energy, Elsevier, vol. 87(P2), pages 849-862.
    6. Murray, Robynne E. & Beach, Ryan & Barnes, David & Snowberg, David & Berry, Derek & Rooney, Samantha & Jenks, Mike & Gage, Bill & Boro, Troy & Wallen, Sara & Hughes, Scott, 2021. "Structural validation of a thermoplastic composite wind turbine blade with comparison to a thermoset composite blade," Renewable Energy, Elsevier, vol. 164(C), pages 1100-1107.
    7. Yu, Dong Ok & Kwon, Oh Joon, 2014. "Predicting wind turbine blade loads and aeroelastic response using a coupled CFD–CSD method," Renewable Energy, Elsevier, vol. 70(C), pages 184-196.
    8. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Balancing global and local search in parallel efficient global optimization algorithms," Journal of Global Optimization, Springer, vol. 67(4), pages 873-892, April.
    9. 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.
    10. Jie Zhu & Xiaohui Ni & Xiaomei Shen, 2020. "Aerodynamic and structural optimization of wind turbine blade with static aeroelastic effects," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 15(1), pages 55-64.
    11. Pourrajabian, Abolfazl & Nazmi Afshar, Peyman Amir & Ahmadizadeh, Mehdi & Wood, David, 2016. "Aero-structural design and optimization of a small wind turbine blade," Renewable Energy, Elsevier, vol. 87(P2), pages 837-848.
    12. Liu, Wenyi & Tang, Baoping & Jiang, Yonghua, 2010. "Status and problems of wind turbine structural health monitoring techniques in China," Renewable Energy, Elsevier, vol. 35(7), pages 1414-1418.
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