Dual-Time-Scale Dynamic Active Wake Control Using Stochastic Yaw Optimization and Distributed Axial Induction Optimization for Large-Scale Wind Farm

As the scale of wind farms (WFs) and the number of wind turbines (WTs) continue to increase, the calculation efficiency of centralized active wake control (AWC) methods can not satisfy the real-time performance requirement. To address this issue, a dual-time-scale dynamic AWC strategy for large-scale WF is proposed in this paper. First, a centralized AWC model is developed which incorporates a dual-time-scale control strategy. The optimal yaw angle is obtained by long-term scale stochastic optimization under the time-varying wind conditions to maximize the power generation potential of WF, and different WF operating modes can be achieved by short-term scale dynamic axial induction control. Then, a spectral clustering-based WF partitioning method is introduced. The wake coupling can be obtained by engineering wake model, and the WF can be divided into multiple subregions through efficient matrix computations and eigenvalue analysis. Finally, in order to improve the calculation speed, the centralized AWC optimization problem is decomposed into several smaller subproblems that can be solved in a distributed manner based on the partitioning results. Simulation results demonstrate that the proposed method obtains reasonable WF partitioning, owns excellent power generation and fatigue load suppression performance under time-varying wind conditions, and significantly improves calculation speed.