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Research on the Power Capture and Wake Characteristics of a Wind Turbine Based on a Modified Actuator Line Model

Author

Listed:
  • Feifei Xue

    (College of Energy and Electrical Engineering, HoHai University, Xikang Road 1, Nanjing 210098, China
    Postal Address: No. 1 Xikang Road, Nanjing 210098, China.)

  • Heping Duan

    (Zhejiang Windey Co., Ltd., Hangzhou 310012, China)

  • Chang Xu

    (College of Energy and Electrical Engineering, HoHai University, Xikang Road 1, Nanjing 210098, China)

  • Xingxing Han

    (College of Energy and Electrical Engineering, HoHai University, Xikang Road 1, Nanjing 210098, China)

  • Yanqing Shangguan

    (College of Mechanical and Electrical Engineering, Hohai University, Hohai Road 5, Changzhou 231000, China)

  • Tongtong Li

    (College of Energy and Electrical Engineering, HoHai University, Xikang Road 1, Nanjing 210098, China)

  • Zhefei Fen

    (College of Energy and Electrical Engineering, HoHai University, Xikang Road 1, Nanjing 210098, China)

Abstract

On a wind farm, the wake has an important impact on the performance of the wind turbines. For example, the wake of an upstream wind turbine affects the blade load and output power of the downstream wind turbine. In this paper, a modified actuator line model with blade tips, root loss, and an airfoil three-dimensional delayed stall was revised. This full-scale modified actuator line model with blades, nacelles, and towers, was combined with a Large Eddy Simulation, and then applied and validated based on an analysis of wind turbine wakes in wind farms. The modified actuator line model was verified using an experimental wind turbine. Subsequently, numerical simulations were conducted on two NREL 5 MW wind turbines with different staggered spacing to study the effect of the staggered spacing on the characteristics of wind turbines. The results show that the output power of the upstream turbine stabilized at 5.9 MW, and the output power of the downstream turbine increased. When the staggered spacing is R and 1.5R, both the power and thrust of the downstream turbine are severely reduced. However, the length of the peaks was significantly longer, which resulted in a long-term unstable power output. As the staggered spacing increased, the velocity in the central near wake of the downstream turbine also increased, and the recovery speed at the threshold of the wake slowed down. The modified actuator line model described herein can be used for the numerical simulation of wakes in wind farms.

Suggested Citation

  • Feifei Xue & Heping Duan & Chang Xu & Xingxing Han & Yanqing Shangguan & Tongtong Li & Zhefei Fen, 2022. "Research on the Power Capture and Wake Characteristics of a Wind Turbine Based on a Modified Actuator Line Model," Energies, MDPI, vol. 15(1), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:282-:d:715926
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    References listed on IDEAS

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    1. Fredriksson, Sam T. & Broström, Göran & Bergqvist, Björn & Lennblad, Johan & Nilsson, Håkan, 2021. "Modelling Deep Green tidal power plant using large eddy simulations and the actuator line method," Renewable Energy, Elsevier, vol. 179(C), pages 1140-1155.
    2. Kim, Taewoo & Oh, Sejong & Yee, Kwanjung, 2015. "Improved actuator surface method for wind turbine application," Renewable Energy, Elsevier, vol. 76(C), pages 16-26.
    3. Qian, Yaoru & Wang, Tongguang & Yuan, Yiping & Zhang, Yuquan, 2020. "Comparative study on wind turbine wakes using a modified partially-averaged Navier-Stokes method and large eddy simulation," Energy, Elsevier, vol. 206(C).
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    Cited by:

    1. Antonio Crespo, 2023. "Computational Fluid Dynamic Models of Wind Turbine Wakes," Energies, MDPI, vol. 16(4), pages 1-3, February.
    2. Victor P. Stein & Hans-Jakob Kaltenbach, 2022. "Validation of a Large-Eddy Simulation Approach for Prediction of the Ground Roughness Influence on Wind Turbine Wakes," Energies, MDPI, vol. 15(7), pages 1-25, April.
    3. Yiyang Sun & Xiangwen Wang & Junjie Yang, 2022. "Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction," Energies, MDPI, vol. 15(12), pages 1-17, June.
    4. Wumaier Tuerxun & Chang Xu & Hongyu Guo & Lei Guo & Namei Zeng & Yansong Gao, 2022. "A Wind Power Forecasting Model Using LSTM Optimized by the Modified Bald Eagle Search Algorithm," Energies, MDPI, vol. 15(6), pages 1-19, March.

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