Closed‐Loop Model‐Predictive Wind Farm Flow Control Under Time‐Varying Inflow Using FLORIDyn

ABSTRACT Wind farm flow control has been a key research focus in recent years, driven by the idea that a collectively operating wind farm can outperform individually controlled turbines. Control strategies are predominantly applied in an open‐loop manner, where the current flow conditions are used to look up precomputed steady‐state set points. Closed‐loop control approaches, on the other hand, take measurements from the farm into account and optimize their set points online, which makes them more flexible and resilient. This paper introduces a closed‐loop model‐predictive wind farm controller using the dynamic engineering model FLORIDyn to maximize the energy generated by a ten‐turbine wind farm. The framework consists of an Ensemble Kalman Filter to continuously correct the flow field estimate, as well as a novel optimization strategy. To this end, the paper discusses two dynamic ways to maximize the farm energy and compares this to the current look‐up table industry standard. The framework relies solely on turbine measurements without using a flow field preview. In a 3‐h case study with time‐varying conditions, the derived controllers achieve an overall energy gain of 3% to 4.4% with noise‐free wind direction measurements. If disturbed and biased measurements are used, this performance decreases to 1.9% to 3% over the greedy control baseline with the same measurements. The comparison to look‐up table controllers shows that the closed‐loop framework performance is more robust to disturbed measurements but can only match the performance in noise‐free conditions.