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Optimization of power coefficient on a horizontal axis wind turbine using bem theory

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  • Bavanish, B.
  • Thyagarajan, K.

Abstract

Aerodynamic optimization has widely become a issue of considerable interest to determine the geometry of an aerodynamic configuration amidst certain design constraints. Aerodynamic performance is calculated from a prescribed geometric shape, which is often performed in trial and error method. Numerous design methods are available for the aerodynamic design of the rotor. The goal in optimizing is to maximize the aerodynamic efficiency at a single design wind speed. However, single-design point methods do not automatically lead to the optimum design, since they consider only one point in the total operational range. Moreover they do not implicitly involve considerations on loads which require an experienced designer. The aerodynamic optimization of a Horizontal Axis Wind Turbine is a complex method characterized by numerous trade-off decisions aimed at finding the optimum overall performance. However researcher design the wind turbine is an enormous ways and more often decision-making is very difficult. Commercial turbines have been derived from both theoretical and empirical methods, but there is no clear evidence on which of these is optimal. Turbine blades are optimized with the aim to achieve maximum power coefficient for the given blade with solidity, ratio of coefficient of drag to lift, angle of attack and tip speed ratio. In this article, the blade element theory is used to find the optimum value analytically. The effect of power coefficient for different blade angle, tip speed ratio, ratio of coefficient of drag and coefficient of lift and blade solidity is presented and the optimized set value is obtained.

Suggested Citation

  • Bavanish, B. & Thyagarajan, K., 2013. "Optimization of power coefficient on a horizontal axis wind turbine using bem theory," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 169-182.
    • Handle: RePEc:eee:rensus:v:26:y:2013:i:c:p:169-182
    DOI: 10.1016/j.rser.2013.05.009
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    References listed on IDEAS

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    1. Scherer, Roger, 1999. "Blade design aspects," Renewable Energy, Elsevier, vol. 16(1), pages 1272-1277.
    2. Ammari, H.D. & Al-Maaitah, A., 2003. "Assessment of wind-generation potentiality in Jordan using the site effectiveness approach," Energy, Elsevier, vol. 28(15), pages 1579-1592.
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    Cited by:

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    2. Dong, Yongjun & Guo, Jingfu & Chen, Jianmei & Sun, Chao & Zhu, Wanqiang & Chen, Liwei & Zhang, Xueming, 2021. "Development of a 300 kW horizontal-axis tidal stream energy conversion system with adaptive variable-pitch turbine and direct-drive PMSG," Energy, Elsevier, vol. 226(C).
    3. Xu, Jian & Wang, Longyan & Luo, Zhaohui & Wang, Zilu & Zhang, Bowen & Yuan, Jianping & Tan, Andy C.C., 2024. "Deep learning enhanced fluid-structure interaction analysis for composite tidal turbine blades," Energy, Elsevier, vol. 296(C).
    4. Zhang, Jisheng & Lin, Xiangfeng & Wang, Risheng & Guo, Yakun & Zhang, Can & Zhang, Yuquan, 2020. "Flow structures in wake of a pile-supported horizontal axis tidal stream turbine," Renewable Energy, Elsevier, vol. 147(P1), pages 2321-2334.
    5. Zhiqiang Yang & Minghui Yin & Yan Xu & Zhengyang Zhang & Yun Zou & Zhao Yang Dong, 2016. "A Multi-Point Method Considering the Maximum Power Point Tracking Dynamic Process for Aerodynamic Optimization of Variable-Speed Wind Turbine Blades," Energies, MDPI, vol. 9(6), pages 1-16, May.
    6. 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.
    7. Del Valle Carrasco, Arturo & Valles-Rosales, Delia J. & Mendez, Luis C. & Rodriguez, Manuel I., 2016. "A site-specific design of a fixed-pitch fixed-speed wind turbine blade for energy optimization using surrogate models," Renewable Energy, Elsevier, vol. 88(C), pages 112-119.
    8. Hércules Araújo Oliveira & José Gomes de Matos & Luiz Antonio de Souza Ribeiro & Osvaldo Ronald Saavedra & Jerson Rogério Pinheiro Vaz, 2023. "Assessment of Correction Methods Applied to BEMT for Predicting Performance of Horizontal-Axis Wind Turbines," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    9. Xu, Jian & Wang, Longyan & Yuan, Jianping & Luo, Zhaohui & Wang, Zilu & Zhang, Bowen & Tan, Andy C.C., 2024. "DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade," Renewable Energy, Elsevier, vol. 224(C).
    10. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
    11. Lu, H.W. & Pan, H.Y. & He, L. & Zhang, J.Q., 2016. "Importance analysis of off-grid wind power generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 999-1007.
    12. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.

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