IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v101y2017icp376-386.html
   My bibliography  Save this article

Investigation of the incoming wind vector for improved wind turbine yaw-adjustment under different atmospheric and wind farm conditions

Author

Listed:
  • Cortina, G.
  • Sharma, V.
  • Calaf, M.

Abstract

Regardless of the evolution of wind energy harvesting, the way in which turbines obtain in-situ meteorological information remains the same - i.e. using traditional wind vanes and cup anemometers installed at the turbine's nacelle, right behind the blades. As a result, misalignment with the mean wind vector is common and energy losses up to 4.6% can be experienced as well as increases in loading and structural fatigue. A solution for the near-blade monitoring is to install wind LIDAR devices on the turbines' nacelle. This technique is currently under development as an alternative to traditional in-situ wind anemometry because it can measure the wind vector at substantial distances upwind. But at what upwind distance should they interrogate the atmosphere? and, what is the optimal average time in which to learn about the incoming flow conditions? This work simulates wind fields approaching isolated wind turbines and wind turbine arrays within large wind farms using Large Eddy Simulations. The goal is to investigate the existence of an optimal upstream scanning distance and average time for wind turbines to measure the incoming wind conditions under different ambient atmospheric conditions. Results reveal no significant differences when measuring the incoming wind vector at different upstream distances, regardless of the atmospheric stratification. Within this framework a 30 min readjustment period is observed to perform the best.

Suggested Citation

  • Cortina, G. & Sharma, V. & Calaf, M., 2017. "Investigation of the incoming wind vector for improved wind turbine yaw-adjustment under different atmospheric and wind farm conditions," Renewable Energy, Elsevier, vol. 101(C), pages 376-386.
  • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:376-386
    DOI: 10.1016/j.renene.2016.08.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116307121
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2016.08.011?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. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    2. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
    3. Shuting Wan & Lifeng Cheng & Xiaoling Sheng, 2015. "Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model," Energies, MDPI, vol. 8(7), pages 1-16, June.
    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. Dai, Juchuan & Yang, Xin & Hu, Wei & Wen, Li & Tan, Yayi, 2018. "Effect investigation of yaw on wind turbine performance based on SCADA data," Energy, Elsevier, vol. 149(C), pages 684-696.
    2. Davide Astolfi & Francesco Castellani & Matteo Becchetti & Andrea Lombardi & Ludovico Terzi, 2020. "Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact," Energies, MDPI, vol. 13(9), pages 1-17, May.
    3. Cortina, G. & Calaf, M., 2017. "Turbulence upstream of wind turbines: A large-eddy simulation approach to investigate the use of wind lidars," Renewable Energy, Elsevier, vol. 105(C), pages 354-365.
    4. Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
    5. Yan Pei & Zheng Qian & Bo Jing & Dahai Kang & Lizhong Zhang, 2018. "Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection," Energies, MDPI, vol. 11(3), pages 1-14, March.
    6. Guo, Peng & Chen, Si & Chu, Jingchun & Infield, David, 2020. "Wind direction fluctuation analysis for wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1026-1035.

    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. Jay P. Goit & Wim Munters & Johan Meyers, 2016. "Optimal Coordinated Control of Power Extraction in LES of a Wind Farm with Entrance Effects," Energies, MDPI, vol. 9(1), pages 1-20, January.
    2. Dhiman, Harsh S. & Deb, Dipankar, 2020. "Wake management based life enhancement of battery energy storage system for hybrid wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    3. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
    4. Tian, Runze & Kou, Peng & Zhang, Yuanhang & Mei, Mingyang & Zhang, Zhihao & Liang, Deliang, 2024. "Residual-connected physics-informed neural network for anti-noise wind field reconstruction," Applied Energy, Elsevier, vol. 357(C).
    5. Tanvir Ahmad & Abdul Basit & Muneeb Ahsan & Olivier Coupiac & Nicolas Girard & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms," Energies, MDPI, vol. 12(7), pages 1-15, April.
    6. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2019. "Improving CFD wind farm simulations incorporating wind direction uncertainty," Renewable Energy, Elsevier, vol. 133(C), pages 1011-1023.
    7. Stosic, Tatijana & Telesca, Luciano & Stosic, Borko, 2021. "Multiparametric statistical and dynamical analysis of angular high-frequency wind speed time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    8. Takanori Uchida, 2018. "Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain by Large-Eddy Simulation (LES) Technique," Energies, MDPI, vol. 11(10), pages 1-15, October.
    9. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    10. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    11. Rubel C. Das & Yu-Lin Shen, 2023. "Analysis of Wind Farms under Different Yaw Angles and Wind Speeds," Energies, MDPI, vol. 16(13), pages 1-19, June.
    12. Antonio Colmenar-Santos & Severo Campíez-Romero & Lorenzo Alfredo Enríquez-Garcia & Clara Pérez-Molina, 2014. "Simplified Analysis of the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution Parameters," Energies, MDPI, vol. 7(11), pages 1-30, October.
    13. Souaiby, Marwa & Porté-Agel, Fernando, 2024. "An improved analytical framework for flow prediction inside and downstream of wind farms," Renewable Energy, Elsevier, vol. 225(C).
    14. Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.
    15. Frederik, Joeri A. & van Wingerden, Jan-Willem, 2022. "On the load impact of dynamic wind farm wake mixing strategies," Renewable Energy, Elsevier, vol. 194(C), pages 582-595.
    16. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
    17. 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).
    18. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2020. "Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines," Energy, Elsevier, vol. 205(C).
    19. Jong-Hyeon Shin & Jong-Hwi Lee & Se-Myong Chang, 2019. "A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines," Energies, MDPI, vol. 12(21), pages 1-14, October.
    20. Rivera-Arreba, Irene & Li, Zhaobin & Yang, Xiaolei & Bachynski-Polić, Erin E., 2024. "Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes," Renewable Energy, Elsevier, vol. 221(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:eee:renene:v:101:y:2017:i:c:p:376-386. 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.