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Blade pitch angle control for aerodynamic performance optimization of a wind farm

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  • Lee, Jaejoon
  • Son, Eunkuk
  • Hwang, Byungho
  • Lee, Soogab

Abstract

The power loss of a wind turbine due to wakes from upstream turbines is significant for a wind farm. This power loss is usually about 20% of wind turbine power, and this can increase to 40% for an extreme case. Such effects decrease the annual energy production of a wind farm. Thus, it is important to predict the effect of these types of wakes so as to maximize the power output of a wind farm. In this study, we investigate a method to control the pitch angle of turbines to maximize the aerodynamic power of a wind farm. The pitch angle of each wind turbine is controlled by its own pitch schedule or feedback algorithm to optimize the power output. However, these control methods cannot consider the effects of wakes, such as velocity defects and an increase of the turbulent intensity. Thus, controlling the pitch angle of the turbine does not guarantee the maximum aerodynamic power of the wind farm, which is why a comprehensive control method considering all of the wind turbines of a wind farm is needed. The blade element momentum theory (BEM) is used for the aerodynamic analysis. In addition, in order to evaluate the wind turbine wake, the eddy viscosity model (EVM) is used. The wake is assumed to be a two-dimensional Gaussian profile determined by the thrust coefficients of the fore-located turbines and the atmospheric conditions. A genetic algorithm (GA) was applied to calculate the optimal power. The results show that control of the pitch angle can maximize the power output of the wind farm.

Suggested Citation

  • Lee, Jaejoon & Son, Eunkuk & Hwang, Byungho & Lee, Soogab, 2013. "Blade pitch angle control for aerodynamic performance optimization of a wind farm," Renewable Energy, Elsevier, vol. 54(C), pages 124-130.
    • Handle: RePEc:eee:renene:v:54:y:2013:i:c:p:124-130
    DOI: 10.1016/j.renene.2012.08.048
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    References listed on IDEAS

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    1. González, Javier Serrano & Gonzalez Rodriguez, Angel G. & Mora, José Castro & Santos, Jesús Riquelme & Payan, Manuel Burgos, 2010. "Optimization of wind farm turbines layout using an evolutive algorithm," Renewable Energy, Elsevier, vol. 35(8), pages 1671-1681.
    2. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
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    2. Wang, Ni & Li, Jian & Yu, Xiang & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal active and reactive power cooperative dispatch strategy of wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 148(C), pages 113-123.
    3. Rocha, P.A. Costa & Carneiro de Araujo, J.W. & Lima, R.J. Pontes & Vieira da Silva, M.E. & Albiero, D. & de Andrade, C.F. & Carneiro, F.O.M., 2018. "The effects of blade pitch angle on the performance of small-scale wind turbine in urban environments," Energy, Elsevier, vol. 148(C), pages 169-178.
    4. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part I: Model Validation and Near Wake Analysis," Energies, MDPI, vol. 12(5), pages 1-24, March.
    5. De-Zhi Wei & Ni-Na Wang & De-Cheng Wan, 2021. "Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model," Energies, MDPI, vol. 14(15), pages 1-26, July.
    6. Hyungyu Kim & Kwansu Kim & Carlo Luigi Bottasso & Filippo Campagnolo & Insu Paek, 2018. "Wind Turbine Wake Characterization for Improvement of the Ainslie Eddy Viscosity Wake Model," Energies, MDPI, vol. 11(10), pages 1-19, October.
    7. Nouri, Reza & Vasel-Be-Hagh, Ahmad & Archer, Cristina L., 2020. "The Coriolis force and the direction of rotation of the blades significantly affect the wake of wind turbines," Applied Energy, Elsevier, vol. 277(C).
    8. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    9. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    10. Abuaisha, Tareq Saber, 2014. "General study of the control principles and dynamic fault behaviour of variable-speed wind turbine and wind farm generic models," Renewable Energy, Elsevier, vol. 68(C), pages 245-254.
    11. Herp, Jürgen & Poulsen, Uffe V. & Greiner, Martin, 2015. "Wind farm power optimization including flow variability," Renewable Energy, Elsevier, vol. 81(C), pages 173-181.
    12. Javier Serrano González & Bruno López & Martín Draper, 2021. "Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model," Energies, MDPI, vol. 14(4), pages 1-18, February.
    13. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
    14. Ahmet Selim Pehlivan & Beste Bahceci & Kemalettin Erbatur, 2022. "Genetically Optimized Pitch Angle Controller of a Wind Turbine with Fuzzy Logic Design Approach," Energies, MDPI, vol. 15(18), pages 1-15, September.
    15. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.
    16. Yanfang Chen & Young-Hoon Joo & Dongran Song, 2021. "Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach," Energies, MDPI, vol. 14(21), pages 1-24, November.
    17. Lo Brutto, Ottavio A. & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2017. "Assessing the effectiveness of a global optimum strategy within a tidal farm for power maximization," Applied Energy, Elsevier, vol. 204(C), pages 653-666.
    18. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).

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