Differentiable Control for Adaptive Wake Steering

Wake steering yaws upstream wind turbines to deflect their wakes from downstream turbines, increasing the total power produced by the wind farm. Most wake steering methods generate lookup tables offline which map a set of wind farm conditions, such as wind speed, to yaw offset angles for each turbine in a farm. These tables assume all turbines are operational and can be significantly non-optimal when one or more turbines shutdown-as they often do because of low wind speed, routine maintenance, or emergency maintenance. We present a new wake steering method that adapts to turbine status. Using a hybrid model- and learning-based method, differentiable control, we train a neural network to determine yaw offset angles from conditions including turbine status (active/inactive). Unlike the lookup table approach, differentiable control does not solve an optimization problem for each combination of turbine status in a farm; including learning in the method allows it to generalize. We present results for both standard wake steering (all turbines active) and adaptive wake steering (some turbines active). We find that differentiable control has comparable accuracy as and an order of magnitude faster offline compute time than the lookup table approach. Differentiable control enables adaptive wake steering through computationally efficient training and rapid online evaluation.