Predicting wind farm operations with machine learning and the P2D-RANS model: A case study for an AWAKEN site

The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo-2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo-2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm-to-farm interactions are noted, with adverse impacts on power predictions from both models.

Automated summary

This study predicts the power performance and wind velocity field of an onshore wind farm using machine learning models and the pseudo-2D RANS model, and validates the predictions with SCADA data. The machine learning models show improved accuracy in predicting turbine power capture and farm power capture compared to the pseudo-2D RANS model, with lower computational costs. Additionally, the machine learning models provide accurate predictions of wind turbulence intensity at the turbine level, which is difficult to achieve through RANS modeling. Interactions between wind farms are also observed, with adverse impacts on power predictions from both models.