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Research of Turbine Tower Optimization Based on Criterion Method

Author

Listed:
  • Dan Li

    (College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Hongbing Bao

    (College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Ning Zhao

    (College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

Tower cost makes up an important part in the whole wind turbine construction especially for offshore wind farms. The main method to reduce tower cost is to reduce tower weight by optimum design. This paper proposes a two-level optimization criterion method for the optimal design of steel conical tower considering different structural reliability and uncertainty, along with the discreteness of design variables such as tower thickness and bolt type. In the first level, the tower shell geometry can be obtained by section design method; in the second level, bolted connections and flanges are designed based on the results of the first level. Then, summarized analysis and iterative calculation is performed to obtain optimum tower design with constant strength and rigidness. This method will play an important role in offshore customized turbine design.

Suggested Citation

  • Dan Li & Hongbing Bao & Ning Zhao, 2023. "Research of Turbine Tower Optimization Based on Criterion Method," Energies, MDPI, vol. 16(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:906-:d:1034421
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    References listed on IDEAS

    as
    1. Zhu, Jie & Zhou, Zhong & Cai, Xin, 2020. "Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model," Renewable Energy, Elsevier, vol. 150(C), pages 523-537.
    2. Al-Sanad, Shaikha & Wang, Lin & Parol, Jafarali & Kolios, Athanasios, 2021. "Reliability-based design optimisation framework for wind turbine towers," Renewable Energy, Elsevier, vol. 167(C), pages 942-953.
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    Cited by:

    1. Cheng, Biyi & Yao, Yingxue & Qu, Xiaobin & Zhou, Zhiming & Wei, Jionghui & Liang, Ertang & Zhang, Chengcheng & Kang, Hanwen & Wang, Hongjun, 2024. "Multi-objective parameter optimization of large-scale offshore wind Turbine's tower based on data-driven model with deep learning and machine learning methods," Energy, Elsevier, vol. 305(C).

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