Dynamic wake modeling and state estimation for improved model-based receding horizon control of wind farms

Power tracking is an emerging application for wind farm control designs that allows farms to participate in a wider range of grid services, such as secondary frequency regulation. Control designs that enable large wind farms to follow a time-varying power trajectory are complicated by aerodynamic interactions that make it impossible to decouple upstream wind turbine control actions from downstream power production. This coupling is particularly important in applications where the reference trajectory is changing faster than, or at a similar rate as, the propagation of turbine wakes through the farm. In this work we overcome these difficulties by using a dynamic wake model that accounts for wake expansion, advection, and multi-wake interactions within a model-based receding horizon controller for coordinated control of a large multi-turbine wind farm. An ensemble Kalman filter is employed for state estimation and error correction at the turbine level. We implement the controller in high-fidelity numerical simulations of a wind farm with 84 turbines and then test the controlled farm's ability to track a power reference signal. The results demonstrate the ability of the control algorithm to track two types of power reference signals used by a US independent system operator.