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Lidar assisted wake redirection in wind farms: A data driven approach

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  • Dhiman, Harsh S.
  • Deb, Dipankar
  • Foley, Aoife M.

Abstract

Lidar based wind measurement is an integral part of wind farm control. The major issues and challenges in power maximization include the potential losses due to wake effect observed among wind turbines. This manuscript presents a wake management technique that utilizes lidar simulations for wake redirection. The proposed methodology is validated for 2-turbine and 15-turbine wind farm layouts involving a PI control based yaw angle correction. Yaw angle misalignment using wake center tracking of the upstream turbines is used to increase the power generation levels. Results of wake center estimation are compared with a Kalman filter based method. Further, the velocity deficit and overall farm power improvement by yaw angle correction is calculated. Results reveal a 1.7% and 0.675% increase in total wind farm power for two different wind speed cases.

Suggested Citation

  • Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Lidar assisted wake redirection in wind farms: A data driven approach," Renewable Energy, Elsevier, vol. 152(C), pages 484-493.
    • Handle: RePEc:eee:renene:v:152:y:2020:i:c:p:484-493
    DOI: 10.1016/j.renene.2020.01.027
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    Cited by:

    1. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    2. Tang, Shengming & Li, Tiantian & Guo, Yun & Zhu, Rong & Qu, Hongya, 2022. "Correction of various environmental influences on Doppler wind lidar based on multiple linear regression model," Renewable Energy, Elsevier, vol. 184(C), pages 933-947.
    3. Wang, Xuguang & Ren, Huan & Zhai, Junhai & Xing, Hongjie & Su, Jie, 2022. "Adaptive support segment based short-term wind speed forecasting," Energy, Elsevier, vol. 249(C).
    4. 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).
    5. Tang, Shengming & Guo, Yun & Wang, Xu & Zhu, Rong & Tang, Jie & Zhang, Shuai, 2023. "Evaluation and impact factors of Doppler wind lidar during Super Typhoon Lekima (2019)," Renewable Energy, Elsevier, vol. 205(C), pages 305-316.
    6. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
    8. Dhiman, Harsh S. & Deb, Dipankar, 2020. "Wake management based life enhancement of battery energy storage system for hybrid wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).

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