Extending a row-averaged model to a turbine-specific model for wind farm control

This study builds upon a recently proposed model-based receding horizon control approach that enables wind farms to follow a reference power signal. In particular, we extend the underlying wake model to enable control of individual turbines in order to both generalize the approach to arbitrary wind farm configurations and account for spatially heterogeneous inflow conditions. We also develop the associated estimation techniques to account for the spatially varying inflow conditions within the control loop. The additional control authority introduced through the individual turbine control has the potential to improve the performance of the control over a wide range of wind conditions. Results demonstrate that accounting for the local wind farm conditions leads to improved estimates of the flow field and the ability to reproduce the transient behavior of the flow field in irregularly arranged wind turbine arrays. Finally, the trade-offs between the improvements in power tracking and estimation accuracy and the increased computational complexity of using the individual turbine versus the aggregate row approach are evaluated.