Design and evaluation of EnKF-based estimators for steady-state-model-based wind farm control

In the field of wind farm flow control and monitoring, most studies rely on steady-state engineering models. Most of these models are based on a statistical description of wakes and assume uniform wind conditions across the farm. They take as inputs at least the free-flow wind conditions, possibly the yaw misalignment angles between the rotor axis, wind direction and some parameters of the wake models, which can also vary. This work introduces an application of Ensemble Kalman Filters, where they are used to estimate the inputs required by a steady-state wind farm solver to align with a set of uncertain or biased measurements from a wind farm SCADA system. While the primary objective is estimating the free-flow wind conditions, strategies are proposed to also estimate biases in the nacelle and wind vane position measurements, potentially enabling their detection and compensation without the need for additional sensors. The performance of the EnKFs is evaluated using both synthetic and real SCADA data, with heuristic free-flow wind estimators serving as benchmarks.