scholarly journals Statistical prediction of far-field wind-turbine noise, with probabilistic characterization of atmospheric stability

2018 ◽  
Vol 10 (1) ◽  
pp. 013302 ◽  
Author(s):  
Mark Kelly ◽  
Emre Barlas ◽  
Andrey Sogachev
Keyword(s):  
Wind Turbine ◽  
Atmospheric Stability ◽  
Statistical Prediction ◽  
Far Field ◽  
Wind Turbine Noise ◽  
Probabilistic Characterization

scholarly journals Volumetric Lidar Scanning of Wind Turbine Wakes under Convective and Neutral Atmospheric Stability Regimes

2014 ◽  
Vol 31 (10) ◽  
pp. 2035-2048 ◽  
Author(s):  
Giacomo Valerio Iungo ◽  
Fernando Porté-Agel
Keyword(s):  
Thermal Stability ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Velocity ◽  
Turbulence Intensity ◽  
Atmospheric Stability ◽  
Power Harvesting ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract Optimization of a wind farm’s layout is a strategic task to reduce wake effects on downstream turbines, thus maximizing wind power harvesting. However, downstream evolution and recovery of each wind turbine wake are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as the vertical profiles of the mean wind velocity and the turbulence intensity, which are in turn affected by the ABL thermal stability. Therefore, the characterization of the variability of wind turbine wakes under different ABL stability regimes becomes fundamental to better predict wind power harvesting and to improve wind farm efficiency. To this aim, wind velocity measurements of the wake produced by a 2-MW Enercon E-70 wind turbine were performed with three scanning Doppler wind lidars. One lidar was devoted to the characterization of the incoming wind—in particular, wind velocity, shear, and turbulence intensity at the height of the rotor disc. The other two lidars performed volumetric scans of the wind turbine wake under different atmospheric conditions. Through the evaluation of the minimum wake velocity deficit as a function of the downstream distance, it is shown that the ABL stability regime has a significant effect on the wake evolution; in particular, the wake recovers faster under convective conditions. This result suggests that atmospheric inflow conditions, and particularly thermal stability, should be considered for improved wake models and predictions of wind power harvesting.

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