IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v162y2020icp788-801.html
   My bibliography  Save this article

A preliminary numerical study of the wake effects on the fatigue load for wind farm based on elastic actuator line model

Author

Listed:
  • Meng, Hang
  • Li, Li
  • Zhang, Jinhua

Abstract

Wake effect commonly exists in the current large wind farms, which will cause not only 10–20% decline of whole power production but also 5–15% increase of fatigue loading on the wind turbine rotor. As a result, it is an important topic to quantify wake-induced fatigue damage in wind farm. In the current research, coupled with precursor method, elastic actuator line model was employed to simulate the fatigue damage loading on the wind turbine blades in wind farm. An obvious peak shift can be found between the power spectrum of upstream and downstream wind turbines, which is induced by the wake turbulence. From the simulation results, it was found that the high amplitude loading cycles of downstream wind turbine is much higher than that of upstream one, which will definitely cause the fatigue damage on the blades. This research work is a preliminary research for the wake-induced fatigue damage by elastic actuator line model. More quantitative study about the wake-induced fatigue damage will be carried out in the future work.

Suggested Citation

  • Meng, Hang & Li, Li & Zhang, Jinhua, 2020. "A preliminary numerical study of the wake effects on the fatigue load for wind farm based on elastic actuator line model," Renewable Energy, Elsevier, vol. 162(C), pages 788-801.
  • Handle: RePEc:eee:renene:v:162:y:2020:i:c:p:788-801
    DOI: 10.1016/j.renene.2020.07.140
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120312167
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2020.07.140?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. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    2. Kim, Soo-Hyun & Shin, Hyung-Ki & Joo, Young-Chul & Kim, Keon-Hoon, 2015. "A study of the wake effects on the wind characteristics and fatigue loads for the turbines in a wind farm," Renewable Energy, Elsevier, vol. 74(C), pages 536-543.
    3. Cheng, Yu & Zhang, Mingming & Zhang, Ziliang & Xu, Jianzhong, 2019. "A new analytical model for wind turbine wakes based on Monin-Obukhov similarity theory," Applied Energy, Elsevier, vol. 239(C), pages 96-106.
    4. Sørensen, Jens Nørkær & Nilsson, Karl & Ivanell, Stefan & Asmuth, Henrik & Mikkelsen, Robert Flemming, 2020. "Analytical body forces in numerical actuator disc model of wind turbines," Renewable Energy, Elsevier, vol. 147(P1), pages 2259-2271.
    5. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    6. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    7. Meng, Hang & Lien, Fue-Sang & Li, Li, 2018. "Elastic actuator line modelling for wake-induced fatigue analysis of horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 116(PA), pages 423-437.
    8. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    9. 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.
    10. Kuo, Jim Y.J. & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2016. "Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming," Applied Energy, Elsevier, vol. 178(C), pages 404-414.
    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. Chanprasert, W. & Sharma, R.N. & Cater, J.E. & Norris, S.E., 2022. "Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence," Renewable Energy, Elsevier, vol. 190(C), pages 208-222.
    2. Liu, Weiqi & Shi, Jian & Chen, Hailong & Liu, Hengxu & Lin, Zi & Wang, Lingling, 2021. "Lagrangian actuator model for wind turbine wake aerodynamics," Energy, Elsevier, vol. 232(C).
    3. Liu, Songyue & Li, Qiusheng & Lu, Bin & He, Junyi, 2024. "Analysis of NREL-5MW wind turbine wake under varied incoming turbulence conditions," Renewable Energy, Elsevier, vol. 224(C).

    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. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
    2. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
    3. 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).
    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. Zhang, Ziyu & Huang, Peng, 2023. "Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model," Renewable Energy, Elsevier, vol. 219(P1).
    6. Cheng, Yu & Zhang, Mingming & Zhang, Ziliang & Xu, Jianzhong, 2019. "A new analytical model for wind turbine wakes based on Monin-Obukhov similarity theory," Applied Energy, Elsevier, vol. 239(C), pages 96-106.
    7. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    8. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    9. Gu, Bo & Meng, Hang & Ge, Mingwei & Zhang, Hongtao & Liu, Xinyu, 2021. "Cooperative multiagent optimization method for wind farm power delivery maximization," Energy, Elsevier, vol. 233(C).
    10. 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.
    11. 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.
    12. Zhang, Huan & Ge, Mingwei & Liu, Yongqian & Yang, Xiang I.A., 2021. "A new coupled model for the equivalent roughness heights of wind farms," Renewable Energy, Elsevier, vol. 171(C), pages 34-46.
    13. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    14. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    15. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    16. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions," Applied Energy, Elsevier, vol. 242(C), pages 1383-1395.
    17. Zhang, Shaohai & Gao, Xiaoxia & Ma, Wanli & Lu, Hongkun & Lv, Tao & Xu, Shinai & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu, 2023. "Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function," Renewable Energy, Elsevier, vol. 215(C).
    18. Yang, Shanghui & Deng, Xiaowei & Yang, Kun, 2024. "Machine-learning-based wind farm optimization through layout design and yaw control," Renewable Energy, Elsevier, vol. 224(C).
    19. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    20. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).

    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:162:y:2020:i:c:p:788-801. 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.