Posters

Abstract

In the wind power industry, the precise prediction of power output based on wind conditions is crucial for assessing the viability of business cases. Traditionally, this prediction relies on simplifying assumptions, such as the belief that wind speed at hub height adequately represents the kinetic energy across the entire rotor plane, or that the vertical wind speed profile can be characterized by a shear exponent value using the power law. However, measurements from advanced tools like remote sensing devices or well-equipped tall meteorological masts often reveal the limitations of these assumptions, leading to inaccurate power production forecasts. Incorporating additional measured data beyond hub-height wind speed into power curve assessments enhances the accuracy of these predictions, thereby adding value to wind farm business cases. This approach is relevant not only in complex terrains but also in flat and offshore environments. This study explores various methodologies for enhancing power curve measurements by addressing sources of error such as deviations in kinetic energy, aerodynamic efficiency, turbulence intensity, and wind turbine controller behavior. By applying these methodologies to real-world measurements, the study demonstrates significant improvements in the consistency and accuracy of power production predictions. This underscores the necessity of considering more comprehensive wind data, rather than relying solely on hub-height wind speed. The findings highlight the importance of advanced measurement techniques in refining power output predictions, ultimately contributing to more robust business cases for wind farms.