21 May 2021

21 May 2021

Review status: this preprint is currently under review for the journal WES.

A physically interpretable statistical wake steering model

Balthazar Sengers1, Matthias Zech2, Pim Jacobs1, Gerald Steinfeld1, and Martin Kühn1 Balthazar Sengers et al.
  • 1ForWind, Institute of Physics, Carl von Ossietzky University Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
  • 2German Aerospace Center (DLR), Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany

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.

Balthazar Sengers et al.

Status: open (extended)

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Balthazar Sengers et al.

Model code and software

Model code statistical wake steering model Balthazar Sengers, Matthias Zech

Balthazar Sengers et al.


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Short summary
Wake steering aims to redirect the wake away from a downstream turbine. This study explores the potential of a data-driven model whose equations can be interpreted physically. It estimates wake characteristics from measurable input variables by utilizing a simple linear model. The model shows encouraging results in estimating available power in the far wake, with significant improvements over currently used analytical models in conditions where wake steering is deemed most effective.