IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v105y2017icp354-365.html
   My bibliography  Save this article

Turbulence upstream of wind turbines: A large-eddy simulation approach to investigate the use of wind lidars

Author

Listed:
  • Cortina, G.
  • Calaf, M.

Abstract

Despite the evolution of wind turbines, the way in which in-situ meteorological information is obtained has not evolved much. Wind vane and cup anemometers, installed at the turbines nacelle, right behind the blades, are still used. This near-blade monitoring does not provide any time to readjust the profile of the wind turbine, and subjects the blades and structure to wind gusts and extreme incoming wind conditions. A solution is to install wind lidar devices on the turbine's nacelle. This technique is currently under development as an alternative to traditional in-situ wind anemometry because it can measure the wind vector at substantial distances upwind. However, most used wind lidar systems are optimized for measuring within a fixed upwind range, but at what upwind distance should they interrogate the atmosphere? This work uses Large Eddy Simulations to create a realistic atmospheric flow to evaluate optimal scanning distances to learn about the incoming turbulence as a function of wind farm configuration and atmospheric stratification. A correlation model, based on a modified truncated normal distribution, has also been developed, which could be implemented within the feed-forward collective pitch control of the turbine, allowing for improved wind turbine readjustments.

Suggested Citation

  • Cortina, G. & Calaf, M., 2017. "Turbulence upstream of wind turbines: A large-eddy simulation approach to investigate the use of wind lidars," Renewable Energy, Elsevier, vol. 105(C), pages 354-365.
    • Handle: RePEc:eee:renene:v:105:y:2017:i:c:p:354-365
    DOI: 10.1016/j.renene.2016.12.069
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116311284
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.12.069?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Cortina, G. & Sharma, V. & Calaf, M., 2017. "Investigation of the incoming wind vector for improved wind turbine yaw-adjustment under different atmospheric and wind farm conditions," Renewable Energy, Elsevier, vol. 101(C), pages 376-386.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Gaurier, Benoît & Ikhennicheu, Maria & Germain, Grégory & Druault, Philippe, 2020. "Experimental study of bathymetry generated turbulence on tidal turbine behaviour," Renewable Energy, Elsevier, vol. 156(C), pages 1158-1170.
    3. Yazhou Wang & Xin Cai & Shifa Lin & Bofeng Xu & Yuan Zhang & Saixian Bian, 2022. "Study of Tower Clearance Safety Protection during Extreme Gust Based on Wind Turbine Monitoring Data," Energies, MDPI, vol. 15(12), pages 1-11, June.
    4. Cortina, G. & Sharma, V. & Torres, R. & Calaf, M., 2020. "Mean kinetic energy distribution in finite-size wind farms: A function of turbines’ arrangement," Renewable Energy, Elsevier, vol. 148(C), pages 585-599.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    2. Ziyu Zhang & Peng Huang & Haocheng Sun, 2020. "A Novel Analytical Wake Model with a Cosine-Shaped Velocity Deficit," Energies, MDPI, vol. 13(13), pages 1-20, June.
    3. Zhang, Shaohai & Duan, Huanfeng & Lu, Lin & He, Ruiyang & Gao, Xiaoxia & Zhu, Songye, 2024. "Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy," Energy, Elsevier, vol. 294(C).
    4. Ahmadi, Mohammad H.B., 2019. "Influence of upstream turbulence on the wake characteristics of a tidal stream turbine," Renewable Energy, Elsevier, vol. 132(C), pages 989-997.
    5. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    6. Razi, P. & Ramaprabhu, P. & Tarey, P. & Muglia, M. & Vermillion, C., 2022. "A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions," Renewable Energy, Elsevier, vol. 200(C), pages 1602-1617.
    7. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    8. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    9. Sina Shamsoddin & Fernando Porté-Agel, 2014. "Large Eddy Simulation of Vertical Axis Wind Turbine Wakes," Energies, MDPI, vol. 7(2), pages 1-23, February.
    10. Ge, Mingwei & Gayme, Dennice F. & Meneveau, Charles, 2021. "Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building," Renewable Energy, Elsevier, vol. 163(C), pages 1063-1077.
    11. Dar, Arslan Salim & Armengol Barcos, Guillem & Porté-Agel, Fernando, 2022. "An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment," Renewable Energy, Elsevier, vol. 193(C), pages 1049-1061.
    12. Souaiby, Marwa & Porté-Agel, Fernando, 2024. "An improved analytical framework for flow prediction inside and downstream of wind farms," Renewable Energy, Elsevier, vol. 225(C).
    13. Antonini, Enrico G.A. & Caldeira, Ken, 2021. "Atmospheric pressure gradients and Coriolis forces provide geophysical limits to power density of large wind farms," Applied Energy, Elsevier, vol. 281(C).
    14. Pryor, Sara C. & Barthelmie, Rebecca J., 2024. "Wind shadows impact planning of large offshore wind farms," Applied Energy, Elsevier, vol. 359(C).
    15. Mingqiu Liu & Zhichang Liang & Haixiao Liu, 2022. "Numerical Investigations of Wake Expansion in the Offshore Wind Farm Using a Large Eddy Simulation," Energies, MDPI, vol. 15(6), pages 1-19, March.
    16. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    17. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    18. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    19. 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.
    20. Li, Li & Wang, Bing & Ge, Mingwei & Huang, Zhi & Li, Xintao & Liu, Yongqian, 2023. "A novel superposition method for streamwise turbulence intensity of wind-turbine wakes," Energy, Elsevier, vol. 276(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:105:y:2017:i:c:p:354-365. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.