IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v256y2026ipgs0960148125021871.html

Identification of wind inflow characteristics from nacelle lidar measurements in the induction zone of a 9 MW wind turbine

Author

Listed:
  • Fang, Yufeng
  • Li, Cuiping
  • Liu, Lei
  • Guo, Feng
  • Gao, Zhen

Abstract

For large rotor wind turbines, identifying inflow wind characteristics is crucial for their design and operation. During the design stage, anemometers are commonly used to observe wind shear, turbulence spectra, coherence, and turbulence intensity. However, for large wind turbines, anemometers often take measurements below the hub height, leading to discrepancies between actual wind conditions above the hub height and those assumed during the design stage. This makes it challenging to achieve a closed-loop design and compromises the safe operation of large-scale wind turbines. Nacelle-based wind lidar systems can observe wind conditions above and below the hub height in front of the rotor, providing a wind preview for feed-forward control. However, lidar systems designed for feed-forward control often take measurements within the rotor’s induction zone, where wind speeds are lower in regions closer to the rotor. State-of-the-art wind turbine simulations are primarily based on blade element momentum theory. Both wind turbine controller design and load validation require free-stream incoming wind characteristics as input conditions. Therefore, this paper presents a method for identifying free-stream inflow wind characteristics using a lidar system with a maximum measurement distance of 200m in front of the rotor. The study is based on a 9MW turbine with a rotor diameter of 230m and is of great significance for guiding the wind energy industry in both load verification and feed-forward control improvements using nacelle lidar.

Suggested Citation

  • Fang, Yufeng & Li, Cuiping & Liu, Lei & Guo, Feng & Gao, Zhen, 2026. "Identification of wind inflow characteristics from nacelle lidar measurements in the induction zone of a 9 MW wind turbine," Renewable Energy, Elsevier, vol. 256(PG).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pg:s0960148125021871
    DOI: 10.1016/j.renene.2025.124523
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125021871
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.124523?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Han & Li, Yunzhou & Yan, Jie & Xiao, Wuyang & Han, Shuang & Liu, Yongqian, 2025. "A novel minute-scale prediction method of incoming wind conditions with limited LiDAR data," Renewable Energy, Elsevier, vol. 240(C).
    2. Guo, Feng & Mann, Jakob & Peña, Alfredo & Schlipf, David & Cheng, Po Wen, 2022. "The space-time structure of turbulence for lidar-assisted wind turbine control," Renewable Energy, Elsevier, vol. 195(C), pages 293-310.
    3. Sebastiani, Alessandro & Peña, Alfredo & Troldborg, Niels, 2023. "Numerical evaluation of multivariate power curves for wind turbines in wakes using nacelle lidars," Renewable Energy, Elsevier, vol. 202(C), pages 419-431.
    4. Shin, Dongheon & Ko, Kyungnam, 2022. "Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance," Energy, Elsevier, vol. 243(C).
    5. Hamilton, Nicholas & Doubrawa, Paula & Moriarty, Patrick & Letizia, Stefano & Thedin, Regis, 2025. "Modal dynamics of wind turbine wake meandering from lidar observations," Renewable Energy, Elsevier, vol. 254(C).
    6. Asmuth, Henrik & Navarro Diaz, Gonzalo P. & Madsen, Helge Aagaard & Branlard, Emmanuel & Meyer Forsting, Alexander R. & Nilsson, Karl & Jonkman, Jason & Ivanell, Stefan, 2022. "Wind turbine response in waked inflow: A modelling benchmark against full-scale measurements," Renewable Energy, Elsevier, vol. 191(C), pages 868-887.
    7. Guo, Feng & Peña, Alfredo & Gao, Zhen, 2026. "Effects of turbulence inhomogeneity and atmosphere stability on the aeroelastic response of the IEA 22 MW wind turbine," Renewable Energy, Elsevier, vol. 256(PF).
    8. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.
    9. Sebastiani, Alessandro & Angelou, Nikolas & Peña, Alfredo, 2024. "Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar," Applied Energy, Elsevier, vol. 364(C).
    Full references (including those not matched with items on IDEAS)

    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. Shin, Dongheon & Ko, Kyungnam, 2022. "Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance," Energy, Elsevier, vol. 243(C).
    2. Chen, Bowen & Lin, Yonggang & Gu, Yajing & Feng, Xiangheng & Cao, Zhongpeng & Sun, Yong, 2025. "A novel active wake control strategy based on LiDAR for wind farms," Energy, Elsevier, vol. 317(C).
    3. Mohsen Vahidzadeh & Corey D. Markfort, 2019. "Modified Power Curves for Prediction of Power Output of Wind Farms," Energies, MDPI, vol. 12(9), pages 1-19, May.
    4. Xiaodong Wang & Yunong Liu & Luyao Wang & Lin Ding & Hui Hu, 2019. "Numerical Study of Nacelle Wind Speed Characteristics of a Horizontal Axis Wind Turbine under Time-Varying Flow," Energies, MDPI, vol. 12(20), pages 1-19, October.
    5. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    6. Panda, Debasis & Chakraborty, Arunasis & Sarkar, Saptarshi & Johansson, Håkan, 2026. "Rotor effective wind field estimation and multi-body dynamic characterization of a 45 kW horizontal axis wind turbine," Renewable Energy, Elsevier, vol. 256(PF).
    7. Chanprasert, W. & Sharma, R.N. & Cater, J.E. & Norris, S.E., 2022. "Large Eddy Simulation of wind turbine wake interaction in directionally sheared inflows," Renewable Energy, Elsevier, vol. 201(P1), pages 1096-1110.
    8. Jing Zhang & Jixing Chen & Hao Liu & Yining Chen & Jingwen Yang & Zongtao Yuan & Qingan Li, 2023. "Applicability of WorldCover in Wind Power Engineering: Application Research of Coupled Wake Model Based on Practical Project," Energies, MDPI, vol. 16(5), pages 1-16, February.
    9. Sebastiani, Alessandro & Angelou, Nikolas & Peña, Alfredo, 2024. "Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar," Applied Energy, Elsevier, vol. 364(C).
    10. José Baptista & Beatriz Jesus & Adelaide Cerveira & Eduardo J. Solteiro Pires, 2023. "Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses," Sustainability, MDPI, vol. 15(13), pages 1-22, June.
    11. Hongtao Niu & Congxin Yang & Yin Wang, 2023. "Experimental Study on the Influence of Incoming Flow on Wind Turbine Power and Wake Based on Wavelet Analysis," Energies, MDPI, vol. 16(16), pages 1-15, August.
    12. Wen, Jiahao & Zhou, Lei & Zhang, Hongfu, 2023. "Mode interpretation of blade number effects on wake dynamics of small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 263(PA).
    13. Liu, Tianhao & Lv, Kunye & Chen, Fengjie & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Hu, Ruifeng & Jiang, Meihui & Zhang, Dongdong, 2024. "Wind power curve model combining smoothed spline with first-order moments and density-adjusted wind speed strategy," Energy, Elsevier, vol. 313(C).
    14. Zhang, Juntao & Cheng, Chuntian & Yu, Shen, 2024. "Recognizing the mapping relationship between wind power output and meteorological information at a province level by coupling GIS and CNN technologies," Applied Energy, Elsevier, vol. 360(C).
    15. Beatriz Cañadillas & Richard Foreman & Gerald Steinfeld & Nick Robinson, 2023. "Cumulative Interactions between the Global Blockage and Wake Effects as Observed by an Engineering Model and Large-Eddy Simulations," Energies, MDPI, vol. 16(7), pages 1-24, March.
    16. Lu, Hongkun & Gao, Xiaoxia & Yu, Jinxiao & Zhao, Qiansheng & Zhu, Xiaoxun & Ma, Wanli & Cao, Jingyuan & Wang, Yu, 2025. "Analysis and prediction of incoming wind speed for turbines in complex wind farm: Accounting for meteorological factors and spatiotemporal characteristics of wind farm," Applied Energy, Elsevier, vol. 381(C).
    17. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    18. Yanhui Qiao & Yongqian Liu & Yang Chen & Shuang Han & Luo Wang, 2022. "Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences," Energies, MDPI, vol. 15(5), pages 1-25, February.
    19. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    20. Mohsen Vahidzadeh & Corey D. Markfort, 2020. "An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions," Energies, MDPI, vol. 13(4), pages 1-23, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:256:y:2026:i:pg:s0960148125021871. 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.