Modeling the effect of wind speed and direction shear on utility-scale wind turbine power production

TL;DR

This study evaluates three models for predicting utility-scale wind turbine power output considering wind shear effects, using LiDAR measurements and real turbine data, highlighting the blade element model's superior accuracy.

Contribution

It introduces and compares three wind power prediction models that incorporate wind shear effects, demonstrating the importance of complex wind profiles on power output.

Findings

Blade element model shows higher correlation and lower error.

Wind shear causes power variation of -19% to +34%.

Non-monotonic wind profiles significantly impact power predictions.

Abstract

Wind speed and direction variations across the rotor affect power production. As utility-scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility-scale wind turbine. In the field measurements of the utility-scale turbine, discrete combinations of speed and direction shear induce changes in power production of -19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over-performance and positive direction shear to under-performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub-models. To further assess the influence of complex, non-monotonic wind profiles, we also drive the models with best-fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time-varying, non-monotonic wind shear affects wind power production.