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Improving wind turbine blade based on multi-objective particle swarm optimization

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  • Li, Yingjue
  • Wei, Kexiang
  • Yang, Wenxian
  • Wang, Qiong

Abstract

This paper studies a new method for optimizing the design of wind turbine blades. Compared with the existing blade design methods, the proposed method not only considers the structural strength and stiffness of the blade but also considers the noise and power generation efficiency of the blade. The method utilizes a multi-objective particle swarm optimization method and the finite volume method in combination to meet the strength and stiffness requirements of the wind turbine blade, improve its aerodynamic performance and reduce its noise. In the study, the geometries of the blade used by a 2 MW wind turbine are taken as the initial parameters of the target blade and MATLAB and ANSYS are employed to perform the optimization and finite element analysis based performance calculations. Then an intelligent optimization algorithm was developed for achieving a quiet and efficient wind turbine blade. In such a multi-objective optimization algorithm, both structural strength, stiffness, noise reduction, and aerodynamic performance of the blade are taken as objective functions. The simulation results have shown that through optimization, the blade noise was reduced by 3.1 dB and the power coefficient was increased by 6.9%. Moreover, it is found that the blade’s structural strength and stiffness are also improved after optimization. This implies that the proposed algorithm is also helpful to further reduce the manufacturing materials and costs of wind turbine blades.

Suggested Citation

  • Li, Yingjue & Wei, Kexiang & Yang, Wenxian & Wang, Qiong, 2020. "Improving wind turbine blade based on multi-objective particle swarm optimization," Renewable Energy, Elsevier, vol. 161(C), pages 525-542.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:525-542
    DOI: 10.1016/j.renene.2020.07.067
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    References listed on IDEAS

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    1. Peter J. Schubel & Richard J. Crossley, 2012. "Wind Turbine Blade Design," Energies, MDPI, vol. 5(9), pages 1-25, September.
    2. Göçmen, Tuhfe & Özerdem, Barış, 2012. "Airfoil optimization for noise emission problem and aerodynamic performance criterion on small scale wind turbines," Energy, Elsevier, vol. 46(1), pages 62-71.
    3. Aslam Bhutta, Muhammad Mahmood & Hayat, Nasir & Farooq, Ahmed Uzair & Ali, Zain & Jamil, Sh. Rehan & Hussain, Zahid, 2012. "Vertical axis wind turbine – A review of various configurations and design techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1926-1939.
    4. Ghasemian, Masoud & Nejat, Amir, 2015. "Aero-acoustics prediction of a vertical axis wind turbine using Large Eddy Simulation and acoustic analogy," Energy, Elsevier, vol. 88(C), pages 711-717.
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    Cited by:

    1. Sun, Jingqi & Ruze, Nuermaimaiti & Zhang, Jianjun & Shi, Jing & Shen, Boyang, 2021. "Capacity planning and optimization for integrated energy system in industrial park considering environmental externalities," Renewable Energy, Elsevier, vol. 167(C), pages 56-65.
    2. Jia, Liangyue & Hao, Jia & Hall, John & Nejadkhaki, Hamid Khakpour & Wang, Guoxin & Yan, Yan & Sun, Mengyuan, 2021. "A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power," Energy, Elsevier, vol. 215(PA).
    3. Jiang, Zhiyuan & Huang, Xianzhen & Wang, Bingxiang & Liao, Xin & Liu, Huizhen & Ding, Pengfei, 2024. "Time-dependent reliability-based design optimization of main shaft bearings in wind turbines involving mixed-integer variables," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Jinlei Lv & Wenxian Yang & Haiyang Zhang & Daxiong Liao & Zebin Ren & Qin Chen, 2021. "A Feasibility Study to Reduce Infrasound Emissions from Existing Wind Turbine Blades Using a Biomimetic Technique," Energies, MDPI, vol. 14(16), pages 1-18, August.

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