Wind turbine power performance characterization through aeroelastic simulations and virtual nacelle lidar measurements

The power performance of a wind turbine depends on several characteristics of the inflow, such as wind speed, turbulence and wind shear. Additionally, wind turbine control strategies affect the power performance of the wind turbines; under certain conditions one might want to, e.g., intentionally misalign the turbine with respect to the main wind direction. Here, we evaluate whether the accuracy in power performance evaluation can be improved by using multi-dimensional power curves in the form of multivariate polynomial regressions, which define the power output as function of wind speed, turbulence and yaw misalignment. The analysis is conducted on a dataset of virtual power performance measurements, which is generated through aeroelastic simulations combined with a simulator of nacelle lidar measurements. Results show that the multi-dimensional power curves can provide higher accuracy than those derived using the IEC standard for power curve measurements; the error in power prediction is nearly halved compared to that using the IEC standard power curve method. Additionally, we show that nacelle lidar measurements increase the accuracy of the multi-dimensional power curves when compared to using mast-based anemometer measurements.