Accurate prediction of wind turbine wakes is important for more efficient design and operation of wind parks. Volumetric wake measurements of nacelle-mounted Doppler lidars are used to characterize the wake of a full-scale wind turbine and to validate an analytical wake model that incorporates the effect of wind veer. Both, measurements and model prediction, show an elliptical and tilted spanwise cross-section of the wake in the presence of wind veer. The error between model and measurements is reduced compared to a model without the effect of wind veer. The characterization of the downwind velocity deficit development and wake growth is robust. The wake tilt angle can only be determined for elliptical wakes.

Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction.

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Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of wind turbines that can be used for power prediction. The proposed model is tested and compared to traditional power curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and turbulence measurements from a nearby meteorological tower are used in the models. The results for a period of four months showed an improvement of 51% in power prediction accuracy, compared to the standard power curve.

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