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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

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