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Wind Turbine Blade Optimal Design Considering Multi-Parameters and Response Surface Method

Author

Listed:
  • Sang-Lae Lee

    (Korean Register of Shipping, 36, Myeongji Ocean City 9-ro, Gangseo-gu, Busan 46762, Korea)

  • SangJoon Shin

    (Institute of Advanced Aerospace Technology, Department of Mechanical and Aerospace Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

Abstract

Within the framework of blade aerodynamic design, the maximum aerodynamic efficiency, power production, and minimum thrust force are the targets to obtain. This paper describes an improved optimization framework for blade aerodynamic design under realistic conditions, while considering multiple design parameters. The relationship between the objective function and the design parameters, such as the chord length, maximum chord, and twist angle, were obtained by using the second-order response surface methodology (RSM). Moreover, the identified parameters were organized to optimize the aerodynamic design of the blades. Furthermore, the initial and optimized blade geometries were compared and showed that the performance of the optimized blade improved significantly. In fact, the efficiency was increased by approximately 10%, although its thrust was not varied. In addition, to demonstrate the improvement in the resulting optimized blades, the annual energy production (AEP) was estimated when installed in a specific regional location. The result showed a significant improvement when compared to the baseline blades. This result will be extended to a new perspective approach for a more robust optimal design of a wind turbine blade.

Suggested Citation

  • Sang-Lae Lee & SangJoon Shin, 2020. "Wind Turbine Blade Optimal Design Considering Multi-Parameters and Response Surface Method," Energies, MDPI, vol. 13(7), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1639-:d:340355
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    References listed on IDEAS

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    1. Fatehi, Mostafa & Nili-Ahmadabadi, Mahdi & Nematollahi, Omid & Minaiean, Ali & Kim, Kyung Chun, 2019. "Aerodynamic performance improvement of wind turbine blade by cavity shape optimization," Renewable Energy, Elsevier, vol. 132(C), pages 773-785.
    2. Kim, Bumsuk & Kim, Woojune & Lee, Sanglae & Bae, Sungyoul & Lee, Youngho, 2013. "Developement and verification of a performance based optimal design software for wind turbine blades," Renewable Energy, Elsevier, vol. 54(C), pages 166-172.
    3. Sessarego, Matias & Feng, Ju & Ramos-García, Néstor & Horcas, Sergio González, 2020. "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow," Renewable Energy, Elsevier, vol. 146(C), pages 1524-1535.
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    Cited by:

    1. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    2. Wenbin Su & Hongbo Wei & Penghua Guo & Qiao Hu & Mengyuan Guo & Yuanjie Zhou & Dayu Zhang & Zhufeng Lei & Chaohui Wang, 2021. "Research on Hydraulic Conversion Technology of Small Ocean Current Turbines for Low-Flow Current Energy Generation," Energies, MDPI, vol. 14(20), pages 1-19, October.
    3. 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).
    4. Mohammed Debbache & Messaoud Hazmoune & Semcheddine Derfouf & Dana-Alexandra Ciupageanu & Gheorghe Lazaroiu, 2021. "Wind Blade Twist Correction for Enhanced Annual Energy Production of Wind Turbines," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    5. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.

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