Ensemble Kalman filtering for wind field estimation in wind farms

Abstract

Currently, wind farms typically rely on greedy control, in which the individual turbine's structural loading and power are optimized. However, this often appears suboptimal for the whole wind farm. A promising solution is closed-loop wind farm control using state feedback algorithms employing a dynamic model of the flow. This control method is a novelty in wind farms, and has potential to provide a temporally optimal control policy accounting for time-varying inflow conditions and unmodeled dynamics, both often neglected in current methods. An essential building block for state feedback control is a state estimator (observer) that reconstructs the system states for the dynamic model using a small number of measurements. As computational efficiency is critical in real-time control, lower-fidelity models are proposed to be used. In this work, WindFarmObserver (WFObs) is introduced, which is a state estimator relying on the WindFarmSimulator (WFSim) model and an Ensemble Kalman Filter (EnKF). The states of WFSim form the two-dimensional flow field in a wind farm at hub height. WFObs is tested in a two-turbine setup using a high-fidelity simulation model. With a realistic sensor setup where only 1.1% of the to-be-estimated states are measured, WFObs reduces the RMS error by 21% compared to open-loop simulation of WFSim, at a low computational cost of 0.76 s per timestep, a factor 102 faster than the common Extended Kalman Filter.