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Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear

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  • Kim, Dae-Young
  • Kim, Yeon-Hee
  • Kim, Bum-Suk

Abstract

The power generation of wind turbines varies depending on external environmental conditions. To present universal correlations between conditions that affect wind speed and wind turbine power, this study analyzed the effects of three atmospheric factors—atmospheric stability, turbulence intensity (TI), and wind shear exponent (WSE)—on the power performance and annual energy production (AEP) of wind turbines. Additionally, the horizontal and vertical speed components of the 3D sonic anemometer were investigated. At two measurement heights, unstable regimes showed an ascending air current, whereas stable regimes simultaneously showed both ascending and descending currents. Power curves calculated for each atmospheric factor regime revealed that atmospheric stability (200 kW–11 m/s) exhibited the greatest difference, followed by TI (91 kW–11 m/s) and WSE (32 kW–10.5 m/s). Finally, AEP was calculated at the annual mean wind speed of the study site and exhibited variations of 1.4–4% according to the factor regime. The AEP difference due to the change in atmospheric stability was greatest, and AEP was highest in moderately unstable atmospheric conditions. Clearer understanding of the effects of atmospheric factors on the power characteristics and AEP of wind turbines is expected to deliver practical benefits for wind-resource assessment and power-production prediction.

Suggested Citation

  • Kim, Dae-Young & Kim, Yeon-Hee & Kim, Bum-Suk, 2021. "Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear," Energy, Elsevier, vol. 214(C).
  • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220321587
    DOI: 10.1016/j.energy.2020.119051
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    1. Sanz Rodrigo, J. & Borbón Guillén, F. & Gómez Arranz, P. & Courtney, M.S. & Wagner, R. & Dupont, E., 2013. "Multi-site testing and evaluation of remote sensing instruments for wind energy applications," Renewable Energy, Elsevier, vol. 53(C), pages 200-210.
    2. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
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