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A LiDAR-Based Active Yaw Control Strategy for Optimal Wake Steering in Paired Wind Turbines

Author

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  • Esmail Mahmoodi

    (Department of Mechanical Engineering of Biosystems, Shahrood University of Technology, Shahrood 3619995161, Iran
    Center for International Scientific Studies and Collaborations, Ministry of Science, Research and Technology, Tehran 158757788, Iran)

  • Mohammad Khezri

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

  • Arash Ebrahimi

    (Institute 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

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

  • Majid Kamandi

    (Department of Mechanical Engineering of Biosystems, University of Tehran, Karaj 7787131587, Iran)

Abstract

In this study, we investigate a yaw control strategy in a two-turbine wind farm with 3.5 MW turbines, aiming to optimize power management. The wind farm is equipped with a nacelle-mounted multi-plane LiDAR system for wind speed measurements. Using an analytical model and integrating LiDAR and SCADA data, we estimate wake effects and power output. Our results show a 2% power gain achieved through optimal yaw control over a year-long assessment. The wind predominantly blows from the southwest, perpendicular to the turbine alignment. The optimal yaw and power gain depend on wind conditions, with higher turbulence intensity and wind speed leading to reduced gains. The power gain follows a bell curve across the range of wind inflow angles, peaking at 1.7% with a corresponding optimal yaw of 17 degrees at an inflow angle of 12 degrees. Further experiments are recommended to refine the estimates and enhance the performance of wind farms through optimized yaw control strategies, ultimately contributing to the advancement of sustainable energy generation.

Suggested Citation

  • Esmail Mahmoodi & Mohammad Khezri & Arash Ebrahimi & Uwe Ritschel & Majid Kamandi, 2024. "A LiDAR-Based Active Yaw Control Strategy for Optimal Wake Steering in Paired Wind Turbines," Energies, MDPI, vol. 17(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5635-:d:1518355
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    References listed on IDEAS

    as
    1. Mou Lin & Fernando Porté-Agel, 2023. "Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control," Energies, MDPI, vol. 16(6), pages 1-17, March.
    2. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    3. 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.
    4. 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.
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