IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i6p2467-d335193.html
   My bibliography  Save this article

Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements

Author

Listed:
  • Fei Zhao

    (School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Yihan Gao

    (Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong 999077)

  • Tengyuan Wang

    (Department of Power Engineering, North China Electric Power University, Baoding 071003, China)

  • Jinsha Yuan

    (School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Xiaoxia Gao

    (Department of Power Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

To study the wake development characteristics of wind farms in complex terrains, two different types of Light Detection and Ranging (LiDAR) were used to conduct the field measurements in a mountain wind farm in Hebei Province, China. Under two different incoming wake conditions, the influence of wind shear, terrain and incoming wind characteristics on the development trend of wake was analyzed. The results showed that the existence of wind shear effect causes asymmetric distribution of wind speed in the wake region. The relief of the terrain behind the turbine indicated a subsidence of the wake centerline, which had a linear relationship with the topography altitudes. The wake recovery rates were calculated, which comprehensively validated the conclusion that the wake recovery rate is determined by both the incoming wind turbulence intensity in the wake and the magnitude of the wind speed.

Suggested Citation

  • Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2467-:d:335193
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/6/2467/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/6/2467/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Singh, Ronit K. & Ahmed, M. Rafiuddin, 2013. "Blade design and performance testing of a small wind turbine rotor for low wind speed applications," Renewable Energy, Elsevier, vol. 50(C), pages 812-819.
    2. Pankaj K. Jha & Earl P. N. Duque & Jessica L. Bashioum & Sven Schmitz, 2015. "Unraveling the Mysteries of Turbulence Transport in a Wind Farm," Energies, MDPI, vol. 8(7), pages 1-29, June.
    3. Torres Garcia, E. & Aubrun, S. & Coupiac, O. & Girard, N. & Boquet, M., 2019. "Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign," Renewable Energy, Elsevier, vol. 130(C), pages 1-11.
    4. 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).
    5. Han, Xingxing & Liu, Deyou & Xu, Chang & Shen, Wen Zhong, 2018. "Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain," Renewable Energy, Elsevier, vol. 126(C), pages 640-651.
    6. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    7. Xydis, G. & Koroneos, C. & Loizidou, M., 2009. "Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece," Applied Energy, Elsevier, vol. 86(11), pages 2411-2420, November.
    8. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2019. "Validations of three-dimensional wake models with the wind field measurements in complex terrain," Energy, Elsevier, vol. 189(C).
    9. Gao, Xiaoxia & Wang, Tengyuan & Li, Bingbing & Sun, Haiying & Yang, Hongxing & Han, Zhonghe & Wang, Yu & Zhao, Fei, 2019. "Investigation of wind turbine performance coupling wake and topography effects based on LiDAR measurements and SCADA data," Applied Energy, Elsevier, vol. 255(C).
    10. Böhme, Gustavo S. & Fadigas, Eliane A. & Gimenes, André L.V. & Tassinari, Carlos E.M., 2018. "Wake effect measurement in complex terrain - A case study in Brazilian wind farms," Energy, Elsevier, vol. 161(C), pages 277-283.
    11. Li, Qing'an & Maeda, Takao & Kamada, Yasunari & Mori, Naoya, 2017. "Investigation of wake characteristics of a Horizontal Axis Wind Turbine in vertical axis direction with field experiments," Energy, Elsevier, vol. 141(C), pages 262-272.
    12. 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.
    13. Sun, Haiying & Yang, Hongxing, 2018. "Study on an innovative three-dimensional wind turbine wake model," Applied Energy, Elsevier, vol. 226(C), pages 483-493.
    14. Li, Qing’an & Maeda, Takao & Kamada, Yasunari & Mori, Naoya, 2017. "Investigation of wake effects on a Horizontal Axis Wind Turbine in field experiments (Part I: Horizontal axis direction)," Energy, Elsevier, vol. 134(C), pages 482-492.
    15. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
    16. Subramanian, B. & Chokani, N. & Abhari, R.S., 2016. "Aerodynamics of wind turbine wakes in flat and complex terrains," Renewable Energy, Elsevier, vol. 85(C), pages 454-463.
    17. Grassi, Stefano & Junghans, Sven & Raubal, Martin, 2014. "Assessment of the wake effect on the energy production of onshore wind farms using GIS," Applied Energy, Elsevier, vol. 136(C), pages 827-837.
    18. Talavera, Miguel & Shu, Fangjun, 2017. "Experimental study of turbulence intensity influence on wind turbine performance and wake recovery in a low-speed wind tunnel," Renewable Energy, Elsevier, vol. 109(C), pages 363-371.
    19. Kumer, Valerie-M. & Reuder, Joachim & Dorninger, Manfred & Zauner, Rudolf & Grubišić, Vanda, 2016. "Turbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applications," Renewable Energy, Elsevier, vol. 99(C), pages 898-910.
    20. 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.
    21. Barthelmie, R.J. & Pryor, S.C., 2013. "An overview of data for wake model evaluation in the Virtual Wakes Laboratory," Applied Energy, Elsevier, vol. 104(C), pages 834-844.
    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. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.

    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. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
    2. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. 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).
    4. 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).
    5. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    6. 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).
    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. 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).
    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. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    11. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    12. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    13. 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.
    14. 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).
    15. Dongqin Zhang & Yang Liang & Chao Li & Yiqing Xiao & Gang Hu, 2022. "Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation," Energies, MDPI, vol. 15(19), pages 1-26, October.
    16. 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.
    17. Gao, Xiaoxia & Chen, Yao & Xu, Shinai & Gao, Wei & Zhu, Xiaoxun & Sun, Haiying & Yang, Hongxing & Han, Zhonghe & Wang, Yu & Lu, Hao, 2022. "Comparative experimental investigation into wake characteristics of turbines in three wind farms areas with varying terrain complexity from LiDAR measurements," Applied Energy, Elsevier, vol. 307(C).
    18. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.
    19. 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).
    20. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(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:gam:jsusta:v:12:y:2020:i:6:p:2467-:d:335193. 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.