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A novel wake model for yawed wind turbines

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  • Lopez, Daniel
  • Kuo, Jim
  • Li, Ni

Abstract

One of the current major challenges in wind energy is to maximize energy production of wind farms. One approach in this effort is through control of wind turbine wake interactions, since undesirable wake interactions can introduce additional mechanical stresses on turbines, leading to early failures and reduce overall energy production of wind farms. To develop control strategies that can minimize wake interactions, it is essential to simulate wake behaviors accurately and quickly. In this work, a fast and accurate turbine wake model capable of modeling turbine wakes under yaw is presented. This model builds upon the work of existing wake models and is capable of producing results comparable to that of conventional full CFD simulations using a fraction of the computational cost. The accuracy and speed of the proposed model allows for the development of real-time turbine control strategies to maximize power output. The results of the proposed model are validated with previous numerical and experimental data.

Suggested Citation

  • Lopez, Daniel & Kuo, Jim & Li, Ni, 2019. "A novel wake model for yawed wind turbines," Energy, Elsevier, vol. 178(C), pages 158-167.
    • Handle: RePEc:eee:energy:v:178:y:2019:i:c:p:158-167
    DOI: 10.1016/j.energy.2019.04.120
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    References listed on IDEAS

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    1. Sunderland, Keith M. & Narayana, Mahinsasa & Putrus, Ghanim & Conlon, Michael F. & McDonald, Steve, 2016. "The cost of energy associated with micro wind generation: International case studies of rural and urban installations," Energy, Elsevier, vol. 109(C), pages 818-829.
    2. Tian, Linlin & Zhu, Weijun & Shen, Wenzhong & Song, Yilei & Zhao, Ning, 2017. "Prediction of multi-wake problems using an improved Jensen wake model," Renewable Energy, Elsevier, vol. 102(PB), pages 457-469.
    3. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2012. "Wake flow model of wind turbine using particle simulation," Renewable Energy, Elsevier, vol. 41(C), pages 185-190.
    4. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    5. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    6. 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.
    7. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    8. Turner, S.D.O. & Romero, D.A. & Zhang, P.Y. & Amon, C.H. & Chan, T.C.Y., 2014. "A new mathematical programming approach to optimize wind farm layouts," Renewable Energy, Elsevier, vol. 63(C), pages 674-680.
    9. Ramos, Carmen & García, Ana Salomé & Moreno, Blanca & Díaz, Guzmán, 2019. "Small-scale renewable power technologies are an alternative to reach a sustainable economic growth: Evidence from Spain," Energy, Elsevier, vol. 167(C), pages 13-25.
    10. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
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    1. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
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    3. Gu, Bo & Meng, Hang & Ge, Mingwei & Zhang, Hongtao & Liu, Xinyu, 2021. "Cooperative multiagent optimization method for wind farm power delivery maximization," Energy, Elsevier, vol. 233(C).
    4. Jim Kuo & Kevin Pan & Ni Li & He Shen, 2020. "Wind Farm Yaw Optimization via Random Search Algorithm," Energies, MDPI, vol. 13(4), pages 1-15, February.
    5. Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
    6. Guo, Zijian & Liu, Tanghong & Xu, Kai & Wang, Junyan & Li, Wenhui & Chen, Zhengwei, 2020. "Parametric analysis and optimization of a simple wind turbine in high speed railway tunnels," Renewable Energy, Elsevier, vol. 161(C), pages 825-835.
    7. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    8. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    9. Dai, Juchuan & He, Tao & Li, Mimi & Long, Xin, 2021. "Performance study of multi-source driving yaw system for aiding yaw control of wind turbines," Renewable Energy, Elsevier, vol. 163(C), pages 154-171.
    10. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    11. Wang, Bingchen & Ding, Lifu & Xiao, Tannan & Chen, Ying & Lu, Qiuyu, 2025. "A novel analytical wake model for mountain wind farms considering variable surface roughness and wake effects of near-middle region," Renewable Energy, Elsevier, vol. 243(C).
    12. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    13. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    14. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    15. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    16. Qian Li & Danyang Chen & Hangbing Lin & Xiaolei Yang, 2025. "A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines," Energies, MDPI, vol. 18(3), pages 1-17, January.
    17. Cai, Wei & Hu, Yang & Fang, Fang & Yao, Lujin & Liu, Jizhen, 2023. "Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines," Applied Energy, Elsevier, vol. 339(C).
    18. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    19. Arslan Salim Dar & Fernando Porté-Agel, 2022. "An Analytical Model for Wind Turbine Wakes under Pressure Gradient," Energies, MDPI, vol. 15(15), pages 1-13, July.

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