A physically interpretable statistical wake steering model

Abstract. Wake steering models for control purposes are typically based on analytical wake descriptions tuned to match experimental or numerical data. This study explores the potential of a data-driven statistical wake steering model with a high degree of physical interpretation. A linear model trained with large eddy simulation data estimates wake parameters such as deficit, center location and curliness from measurable inflow and turbine variables. These wake parameters are then used to generate vertical cross sections of the wake at desired downstream locations. In a validation against eight boundary layers ranging from neutral to stable conditions, the trajectory, shape and available power of the far wake are accurately estimated. The approach allows the choice of different input parameters, while the accuracy of the power estimates remains largely unchanged. A significant improvement in accuracy is shown in a benchmark study against two analytical wake models, especially under derated operating conditions and stable atmospheric stratifications. While results are encouraging, the model’s sensitivity to training data needs further investigation.