Dynamically Maximizing Power Production of Wind Farm Through Accelerated Distributed Control

This article proposes an active power control strategy for dynamically maximizing power production in waked wind farm (WF) based on an accelerated asynchronous distributed calculation scheme (DCS). By adopting a two-degree-of-freedom (2Dof) active power control strategy based on model predictive control (MPC), the generator speed and pitch angle of wind turbines (WT) can be coordinated to enhance the long-term WF-level cumulative power production and power reserve subject to strong wake effects. The proposed strategy is significantly different from existing model-free control strategies, and can quickly respond to time-varying wind speeds through online feedback of both WT mechanical status and dynamic wake. The proposed strategy can greatly improve the efficiency of solving large-scale optimization problems through two designs: first, using asynchronous DCS to reduce computation time and strengthen robustness to communication failures. Second, the Nesterov acceleration strategy is introduced to significantly reduce the number of iterations and enhance the convergence rate. Additionally, a novel control command issuance strategy is devised to address the issue of wake delay. In a 1000s simulation, the proposed strategy achieves a remarkable improvement of 10.81% and 18.29% in cumulative power production and power reserve of WF, respectively.