IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v140y2019icp994-1003.html
   My bibliography  Save this article

Effects of axial induction control on wind farm energy production - A field test

Author

Listed:
  • van der Hoek, Daan
  • Kanev, Stoyan
  • Allin, Julian
  • Bieniek, David
  • Mittelmeier, Niko

Abstract

Recent years have seen an increased interest in literature in Active Wake Control (AWC) strategies, which aim to reduce wake losses in wind farms in order to increase the energy yield and/or decrease loading. This paper presents the results from field tests with one of the AWC strategies, called axial induction control, at a commercial wind farm. To this end, the pitch angle offset for the most upstream wind turbines were optimized for each wind direction to maximize the power capture of the whole farm. This optimization is performed using the wake model FarmFlow, a code based on parabolized Computational Fluid Dynamics (CFD). After calibration of the wind direction measurement and implementation of the optimized pitch settings, a measurement campaign of one year has been performed. The analysis of the measurements indicate that axial induction control results in increased energy production.

Suggested Citation

  • van der Hoek, Daan & Kanev, Stoyan & Allin, Julian & Bieniek, David & Mittelmeier, Niko, 2019. "Effects of axial induction control on wind farm energy production - A field test," Renewable Energy, Elsevier, vol. 140(C), pages 994-1003.
    • Handle: RePEc:eee:renene:v:140:y:2019:i:c:p:994-1003
    DOI: 10.1016/j.renene.2019.03.117
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014811930429X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.03.117?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. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    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. Chen, Kaixuan & Lin, Jin & Qiu, Yiwei & Liu, Feng & Song, Yonghua, 2022. "Joint optimization of wind farm layout considering optimal control," Renewable Energy, Elsevier, vol. 182(C), pages 787-796.
    2. Wang, Ni & Li, Jian & Yu, Xiang & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal active and reactive power cooperative dispatch strategy of wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 148(C), pages 113-123.
    3. van den Broek, Maarten J. & De Tavernier, Delphine & Sanderse, Benjamin & van Wingerden, Jan-Willem, 2022. "Adjoint optimisation for wind farm flow control with a free-vortex wake model," Renewable Energy, Elsevier, vol. 201(P1), pages 752-765.
    4. Bart Matthijs Doekemeijer & Eric Simley & Paul Fleming, 2022. "Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms," Energies, MDPI, vol. 15(6), pages 1-23, March.

    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. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    2. 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).
    3. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    4. Wang, Tengyuan & Cai, Chang & Wang, Xinbao & Wang, Zekun & Chen, Yewen & Song, Juanjuan & Xu, Jianzhong & Zhang, Yuning & Li, Qingan, 2023. "A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow," Energy, Elsevier, vol. 271(C).
    5. 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.
    6. Francesco Castellani & Marco Buzzoni & Davide Astolfi & Gianluca D’Elia & Giorgio Dalpiaz & Ludovico Terzi, 2017. "Wind Turbine Loads Induced by Terrain and Wakes: An Experimental Study through Vibration Analysis and Computational Fluid Dynamics," Energies, MDPI, vol. 10(11), pages 1-19, November.
    7. Shibuya, Koichiro & Uchida, Takanori, 2023. "Wake asymmetry of yaw state wind turbines induced by interference with wind towers," Energy, Elsevier, vol. 280(C).
    8. Yingming Liu & Yingwei Wang & Xiaodong Wang & Jiangsheng Zhu & Wai Hou Lio, 2019. "Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load," Energies, MDPI, vol. 12(8), pages 1-23, April.
    9. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    10. Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
    11. Padullaparthi, Venkata Ramakrishna & Nagarathinam, Srinarayana & Vasan, Arunchandar & Menon, Vishnu & Sudarsanam, Depak, 2022. "FALCON- FArm Level CONtrol for wind turbines using multi-agent deep reinforcement learning," Renewable Energy, Elsevier, vol. 181(C), pages 445-456.
    12. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
    13. Kanev, Stoyan, 2020. "Dynamic wake steering and its impact on wind farm power production and yaw actuator duty," Renewable Energy, Elsevier, vol. 146(C), pages 9-15.
    14. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    15. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    16. Yin, Xiuxing & Zhang, Wencan & Jiang, Zhansi & Pan, Li, 2020. "Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization," Renewable Energy, Elsevier, vol. 160(C), pages 974-986.
    17. Jennifer Marie Rinker & Esperanza Soto Sagredo & Leonardo Bergami, 2021. "The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake," Energies, MDPI, vol. 14(21), pages 1-18, November.
    18. Stoyan Kanev & Edwin Bot & Jack Giles, 2020. "Wind Farm Loads under Wake Redirection Control," Energies, MDPI, vol. 13(16), pages 1-15, August.
    19. Antonio Cioffi & Claudia Muscari & Paolo Schito & Alberto Zasso, 2020. "A Steady-State Wind Farm Wake Model Implemented in OpenFAST," Energies, MDPI, vol. 13(23), pages 1-16, November.
    20. Can Zhang & Jisheng Zhang & Athanasios Angeloudis & Yudi Zhou & Stephan C. Kramer & Matthew D. Piggott, 2023. "Physical Modelling of Tidal Stream Turbine Wake Structures under Yaw Conditions," Energies, MDPI, vol. 16(4), pages 1-21, February.

    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:140:y:2019:i:c:p:994-1003. 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.