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Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization

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  • Yong Ma

    (School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 518000, China
    College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China)

  • Aiming Zhang

    (School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 518000, China)

  • Lele Yang

    (School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 518000, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China)

  • Chao Hu

    (College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China)

  • Yue Bai

    (College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China)

Abstract

Offshore wind power has become an important trend in global renewable energy development. Based on a particle swarm optimization (PSO) algorithm and FAST program, a time-domain coupled calculation model for a floating wind turbine is established, and a combined optimization design method for the wind turbine’s blade is developed in this paper. The influence of waves on the power of the floating wind turbine is studied in this paper. The results show that, with the increase of wave height, the power fluctuation of the wind turbine increases and the average power of the wind turbine decreases. With the increase of wave period, the power oscillation amplitude of the wind turbine increases, and the power of the wind turbine at equilibrium position decreases. The optimal design of the offshore floating wind turbine blade under different wind speeds is carried out. The results show that the optimum effect of the blades is more obvious at low and mid-low wind speeds than at rated wind speeds. Considering the actual wind direction distribution in the sea area, the maximum power of the wind turbine can be increased by 3.8% after weighted optimization, and the chord length and the twist angle of the blade are reduced.

Suggested Citation

  • Yong Ma & Aiming Zhang & Lele Yang & Chao Hu & Yue Bai, 2019. "Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization," Energies, MDPI, vol. 12(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1972-:d:233601
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    References listed on IDEAS

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    Cited by:

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    2. Paweł Ziółkowski & Łukasz Witanowski & Stanisław Głuch & Piotr Klonowicz & Michel Feidt & Aimad Koulali, 2024. "Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine," Energies, MDPI, vol. 17(12), pages 1-29, June.
    3. Petrović, A. & Đurišić, Ž., 2021. "Genetic algorithm based optimized model for the selection of wind turbine for any site-specific wind conditions," Energy, Elsevier, vol. 236(C).
    4. Francesco Castellani & Davide Astolfi, 2020. "Editorial on Special Issue “Wind Turbine Power Optimization Technology”," Energies, MDPI, vol. 13(7), pages 1-4, April.
    5. Mustafa Kaya, 2019. "A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades," Sustainability, MDPI, vol. 11(16), pages 1-25, August.

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