Multicluster Distributed Optimization Strategy for Turbine Wake Environment

As a crucial component of modern energy systems, wind energy plays a significant role in energy transition. In traditional wind power systems, mutual interference between wind turbines leads to wake effect, adversely impacts the power generation efficiency of wind farms. Herein, a multicluster distributed optimization strategy based on wake‐DBSCAN, specifically designed for environments affected by wake interference is proposed. First, clustering analysis on the wind turbine layout and wind conditions to establish a foundation for efficient distributed computation is performed. Based on the clustering results, wake analysis is conducted to plan and optimize the operational strategy for each wind turbine cluster, resulting in a distributed optimization strategy for their operation. Additionally, simulation experiments are conducted on micrositing and time‐varying wind conditions using real‐world data from a wind farm in the Arua region. The experimental results demonstrate that the proposed algorithm can effectively improve the computational efficiency of wake optimization, while ensuring the effect of the wake optimization algorithm in actual wind farms as much as possible, which is more in line with the wake optimization needs of actual wind farms. The algorithm proposed in this article provides valuable insights into wind turbine operation and maintenance under time‐varying conditions.