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Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model

Author

Listed:
  • De-Zhi Wei

    (Computational Marine Hydrodynamics Lab (CMHL), School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Ni-Na Wang

    (Key Laboratory of Far-Shore Wind Power Technology of Zhejiang Province, Power China Huadong Engineering Corporation Limited, Hangzhou 310014, China)

  • De-Cheng Wan

    (Computational Marine Hydrodynamics Lab (CMHL), School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    Ocean College, Zhejiang University, Zhoushan 316021, China)

Abstract

Yaw-based wake steering control is a potential way to improve wind plant overall performance. For its engineering application, it is crucial to accurately predict the turbine wakes under various yawed conditions within a short time. In this work, a two-dimensional analytical model is proposed for far wake modeling under yawed conditions by taking the self-similarity assumption for the streamwise velocity deficit and skewness angle at hub height. The proposed model can be applied to predict the wake center trajectory, streamwise velocity, and transverse velocity in the far-wake region downstream of a yawed turbine. For validation purposes, predictions by the newly proposed model are compared to wind tunnel measurements and large-eddy simulation data. The results show that the proposed model has significantly high accuracy and outperforms other common wake models. More importantly, the equations of the new proposed model are simple, the wake growth rate is the only parameter to be specified, which makes the model easy to be used in practice.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4494-:d:601145
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    References listed on IDEAS

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    1. Stevens, Richard J.A.M. & Martínez-Tossas, Luis A. & Meneveau, Charles, 2018. "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments," Renewable Energy, Elsevier, vol. 116(PA), pages 470-478.
    2. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    3. Kim, Soo-Hyun & Shin, Hyung-Ki & Joo, Young-Chul & Kim, Keon-Hoon, 2015. "A study of the wake effects on the wind characteristics and fatigue loads for the turbines in a wind farm," Renewable Energy, Elsevier, vol. 74(C), pages 536-543.
    4. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    5. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    6. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    7. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    8. 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.
    9. 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.
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    Cited by:

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