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Multi-objective optimization of wind turbine blades using lifting surface method

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  • Shen, Xin
  • Chen, Jin-Ge
  • Zhu, Xiao-Cheng
  • Liu, Peng-Yin
  • Du, Zhao-Hui

Abstract

This paper describes a multi-objective optimization method for the design of horizontal axis wind turbines using the lifting surface method as the performance prediction model. The aerodynamic code for the design method is based on the lifting surface method with a prescribed wake model for the description of the wake. A multi-objective optimization algorithm approach is employed for the optimization of wind turbine blades with 3D stacking line (swept leaned blades). The (NSGA II) Non-dominated sorting genetic algorithm II is used to facilitate the multi-objective optimization and to find the global optima of high-dimensional problems. The scope of the optimization method is to achieve the best trade off of the following objectives: maximum of annual energy production and minimum of blade loads including thrust and blade rood flap-wise moment. To illustrate how the optimization of the blade is carried out the procedure is applied to NREL Phase VI rotor. The result shows the optimization models can provide more efficient designs.

Suggested Citation

  • Shen, Xin & Chen, Jin-Ge & Zhu, Xiao-Cheng & Liu, Peng-Yin & Du, Zhao-Hui, 2015. "Multi-objective optimization of wind turbine blades using lifting surface method," Energy, Elsevier, vol. 90(P1), pages 1111-1121.
    • Handle: RePEc:eee:energy:v:90:y:2015:i:p1:p:1111-1121
    DOI: 10.1016/j.energy.2015.06.062
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    3. Longfu Luo & Xiaofeng Zhang & Dongran Song & Weiyi Tang & Jian Yang & Li Li & Xiaoyu Tian & Wu Wen, 2018. "Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines," Energies, MDPI, vol. 11(10), pages 1-17, October.
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    6. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
    7. Yadong Jiang & William Finnegan & Tomas Flanagan & Jamie Goggins, 2022. "Optimisation of Highly Efficient Composite Blades for Retrofitting Existing Wind Turbines," Energies, MDPI, vol. 16(1), pages 1-20, December.
    8. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2017. "Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines," Energy, Elsevier, vol. 118(C), pages 1222-1233.
    9. Baniassadi, Amir & Shirinbakhsh, Mehrdad & Torabi, Farschad, 2017. "Multivariate optimization of off-grid wind turbines with variable demand - Case study of a remote commercial building," Renewable Energy, Elsevier, vol. 101(C), pages 1021-1029.
    10. Zhenye Sun & Wei Jun Zhu & Wen Zhong Shen & Wei Zhong & Jiufa Cao & Qiuhan Tao, 2020. "Aerodynamic Analysis of Coning Effects on the DTU 10 MW Wind Turbine Rotor," Energies, MDPI, vol. 13(21), pages 1-19, November.
    11. Zhang, Ye & Deng, Shuanghou & Wang, Xiaofang, 2019. "RANS and DDES simulations of a horizontal-axis wind turbine under stalled flow condition using OpenFOAM," Energy, Elsevier, vol. 167(C), pages 1155-1163.
    12. Vianna Neto, Júlio Xavier & Guerra Junior, Elci José & Moreno, Sinvaldo Rodrigues & Hultmann Ayala, Helon Vicente & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2018. "Wind turbine blade geometry design based on multi-objective optimization using metaheuristics," Energy, Elsevier, vol. 162(C), pages 645-658.
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