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A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines

Author

Listed:
  • Esmail Mahmoodi

    (Department of Mechanical Engineering of Biosystems, Shahrood University of Technology, Shahrood 3619995161, Iran)

  • Mohammad Khezri

    (Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Arash Ebrahimi

    (Chair of Wind Energy Technology, Faculty of Mechanical Engineering and Marine Technologies, University of Rostock, 18051 Rostock, Germany
    IWEN Energy Institute, 18119 Rostock, Germany)

  • Uwe Ritschel

    (Chair of Wind Energy Technology, Faculty of Mechanical Engineering and Marine Technologies, University of Rostock, 18051 Rostock, Germany
    IWEN Energy Institute, 18119 Rostock, Germany)

  • Leonardo P. Chamorro

    (Department of Mechanical Science and Engineering, University of Illinois, Urbana, IL 61801, USA)

  • Ali Khanjari

    (Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA)

Abstract

Wind turbine aerodynamic interactions within wind farms lead to significant energy losses. Optimizing the flow between turbines presents a promising solution to mitigate these losses. While analytical models offer a fundamental approach to understanding aerodynamic interactions, further development and refinement of these models are imperative. We propose a simplified analytical model that combines the Gaussian wake model and the cylindrical vortex induction model to evaluate the interaction between wake and induction zones in 3.5 MW wind turbines with 328 m spacing. The model’s validation is conducted using field data from a nacelle-mounted LiDAR system on the downstream turbine. The ‘Direction to Hub’ parameter facilitates a comparison between the model predictions and LiDAR measurements at distances ranging from 50 m to 300 m along the rotor axis. Overall, the results exhibit reasonable agreement in flow trends, albeit with discrepancies of up to 15° in predicting peak interactions. These deviations are attributed to the single-hat Gaussian shape of the wake model and the absence of wake expansion consideration, which can be revisited to improve model fidelity. The ‘Direction to Hub’ parameter proves valuable for model validation and LiDAR calibration, enabling a detailed flow analysis between turbines. This analytical modeling approach holds promise for enhancing wind farm efficiency by advancing our understanding of turbine interactions.

Suggested Citation

  • Esmail Mahmoodi & Mohammad Khezri & Arash Ebrahimi & Uwe Ritschel & Leonardo P. Chamorro & Ali Khanjari, 2023. "A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines," Energies, MDPI, vol. 16(15), pages 1-13, July.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5710-:d:1206992
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    References listed on IDEAS

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    1. Li, Xuyang & Qiu, Yingning & Feng, Yanhui & Wang, Zheng, 2021. "Wind turbine power prediction considering wake effects with dual laser beam LiDAR measured yaw misalignment," Applied Energy, Elsevier, vol. 299(C).
    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. Mahdi Abkar & Jens Nørkær Sørensen & Fernando Porté-Agel, 2018. "An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes," Energies, MDPI, vol. 11(7), pages 1-10, July.
    4. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    5. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
    6. 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.
    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. Mohammadreza Mohammadi & Majid Bastankhah & Paul Fleming & Matthew Churchfield & Ervin Bossanyi & Lars Landberg & Renzo Ruisi, 2022. "Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow," Energies, MDPI, vol. 15(23), pages 1-16, December.
    9. Keane, Aidan & Nisbet, Iain & Calvo, Gabriele & Pickering, George & Tulloch, Jake & More, Graham & Koronka, Neil, 2022. "Wind farm cumulative induction zone effect and the impact on energy yield estimation," Renewable Energy, Elsevier, vol. 181(C), pages 1209-1222.
    10. Khanjari, Ali & Mahmoodi, Esmail & Ahmadi, Mohammad Hossien, 2020. "Energy and exergy analyzing of a wind turbine in free stream and wind tunnel in CFD domain based on actuator disc technique," Renewable Energy, Elsevier, vol. 160(C), pages 231-249.
    11. Michael F. Howland & Jesús Bas Quesada & Juan José Pena Martínez & Felipe Palou Larrañaga & Neeraj Yadav & Jasvipul S. Chawla & Varun Sivaram & John O. Dabiri, 2022. "Collective wind farm operation based on a predictive model increases utility-scale energy production," Nature Energy, Nature, vol. 7(9), pages 818-827, September.
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