Data-driven distributed power regulation of a wind farm with optimized wind turbine operating points

Active power regulation is essential for wind farm (WF) to perform power reserve and its delivery. Large WFs are required to have such capability as imposed by the grid codes for power system security. The current practice deloads and controls individual wind turbines (WT) without effective and scalable coordination across a WF. In this paper, we propose a fully distributed control framework for the active power regulation of WF integrating a large number of WTs. The proposed framework optimizes the operating points of individual turbines in the torque–speed plane, given the power commands at the WF level. The nonlinear wind aerodynamic equations are approximated by a surrogate model using a data-driven approach, which leads to a mixed-integer reformulation of the optimization problem that is solved in a fully distributed fashion. With the obtained optimal turbine operating points across the WF, individual WTs track the reference trajectory using the existing torque and pitch actuators and guarantee that the requested power is delivered. The case studies comprehensively evaluate the proposed method for delivering the WF power regulation with the desired speed and scalability to fulfill the technical requirements of service. The adaptive reaction against single turbine failures for plug-and-play operation is demonstrated as well. • Distributed framework proposed for wind farm active power regulation. • Neural network surrogate model enables mixed-integer linear programming. • Approach achieves superior scalability over centralized methods. • Plug-and-play capability demonstrated under turbine failure events.