IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i16p4088-d395651.html
   My bibliography  Save this article

Wind Farm Loads under Wake Redirection Control

Author

Listed:
  • Stoyan Kanev

    (TNO, ECN Wind Energy, Westerduinweg 3, 1755LE Petten, The Netherlands)

  • Edwin Bot

    (TNO, ECN Wind Energy, Westerduinweg 3, 1755LE Petten, The Netherlands)

  • Jack Giles

    (RWE Renewables UK Limited, Swindon SN5 6PB, UK)

Abstract

Active wake control (AWC) is a strategy for operating wind farms in such a way as to reduce the wake effects on the wind turbines, potentially increasing the overall power production. There are two concepts to AWC: induction control and wake redirection. The former strategy boils down to down-regulating the upstream turbines in order to increase the wind speed in their wakes. This has generally a positive effect on the turbine loading. The wake redirection concept, which relies on intentional yaw misalignment to move wakes away from downstream turbines, has a much more prominent impact and may lead to increased loading. Moreover, the turbines are typically not designed and certified to operate at large yaw misalignments. Even though the potential upsides in terms of power gain are very interesting, the risk for damage or downtime due to increased loading is seen as the main obstacle preventing large scale implementation of this technology. In order to provide good understanding on the impacts of AWC on the turbine loads, this paper presents the results from an in-depth analysis of the fatigue loads on the turbines of an existing wind farm. Even though for some wind turbine components the fatigue loads do increase for some wind conditions under yaw misalignment, it is demonstrated that the wake-induced loading decreases even more so that the lifetime loads under AWC are generally lower.

Suggested Citation

  • Stoyan Kanev & Edwin Bot & Jack Giles, 2020. "Wind Farm Loads under Wake Redirection Control," Energies, MDPI, vol. 13(16), pages 1-15, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4088-:d:395651
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/16/4088/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/16/4088/
    Download Restriction: no
    ---><---

    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.
    2. Martin Cardaun & Björn Roscher & Ralf Schelenz & Georg Jacobs, 2019. "Analysis of Wind-Turbine Main Bearing Loads Due to Constant Yaw Misalignments over a 20 Years Timespan," Energies, MDPI, vol. 12(9), pages 1-11, May.
    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. Antonio Crespo, 2022. "Wakes of Wind Turbines in Yaw for Wind Farm Power Optimization," Energies, MDPI, vol. 15(18), pages 1-3, September.
    2. Yanfang Chen & Young Hoon Joo & Dongran Song, 2022. "Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation," Energies, MDPI, vol. 15(7), pages 1-24, March.
    3. Dongmyoung Kim & Taesu Jeon & Insu Paek & Daeyoung Kim, 2022. "A Study on Available Power Estimation Algorithm and Its Validation," Energies, MDPI, vol. 15(7), pages 1-14, April.

    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. 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).
    2. 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.
    3. 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).
    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. 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).
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. Shibuya, Koichiro & Uchida, Takanori, 2023. "Wake asymmetry of yaw state wind turbines induced by interference with wind towers," Energy, Elsevier, vol. 280(C).
    12. 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.
    13. Wenting Chen & Hang Liu & Yonggang Lin & Wei Li & Yong Sun & Di Zhang, 2020. "LSTM-NN Yaw Control of Wind Turbines Based on Upstream Wind Information," Energies, MDPI, vol. 13(6), pages 1-23, March.
    14. 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.
    15. 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.
    16. 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.
    17. Mehlan, Felix C. & Nejad, Amir R., 2023. "Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring," Renewable Energy, Elsevier, vol. 212(C), pages 70-81.
    18. 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).
    19. 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).
    20. 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.

    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:gam:jeners:v:13:y:2020:i:16:p:4088-:d:395651. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.