Abstract
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.
Highlights
Renewable energies continue to grow and their contribution to the supply of electricity to the power grid is increasing
Data from a met tower and supervisory control and data acquisition (SCADA) system of a 2.5 MW horizontal axis wind turbine were analyzed during a period of four months, in order to develop new models for predicting wind power generation
The proposed approach uses a curve for axial flow induction factor, instead of the standard power curve generally used for the purpose of power prediction
Summary
Renewable energies continue to grow and their contribution to the supply of electricity to the power grid is increasing. The proposed equivalent wind speed is the result of vertical integration of wind speed measurement at several heights to account for vertical variability of wind speed This idea has been further developed to incorporate the effect of yaw error, density variation and turbulence intensity [2,6]. As the results in [15,16,17,18] suggest, the contribution of vertical fluxes to power generation becomes more significant for wind turbines located in complex terrains or within a wind farm, and the induction curve model may provide improvement in power prediction for complex ABL flow conditions.
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