Feedforward-Feedback wake redirection for wind farm control

TL;DR

This paper introduces a combined feedforward-feedback control framework for wind farms that optimizes power output by adjusting turbine yaw angles and uses lidar measurements for real-time wake redirection, showing promising simulation results.

Contribution

It presents a novel integrated control approach combining steady-state wake modeling with lidar-based feedback for improved wind farm energy production.

Findings

Increased total energy output in simulations

Effective disturbance rejection and model uncertainty adaptation

Promising results for real-world implementation

Abstract

This work presents a combined feedforward-feedback wake redirection framework for wind farm control. The FLORIS wake model, a control-oriented steady-state wake model is used to calculate optimal yaw angles for a given wind farm layout and atmospheric condition. The optimal yaw angles, which maximize the total power output, are applied to the wind farm. Further, the lidar-based closed-loop wake redirection concept is used to realize a local feedback on turbine level. The wake center is estimated from lidar measurements \unit[3]{D} downwind of the wind turbines. The dynamical feedback controllers support the feedforward controller and reject disturbances and adapt to model uncertainties. Altogether, the total framework is presented and applied to a nine turbine wind farm test case. In a high fidelity simulation study the concept shows promising results and an increase in total energy production compared to the baseline case and the feedforward-only case.