Active yaw control of an offshore wind farm cluster for maximizing collective power output considering inter-farm and inner-array wake effects

With the large-scale development of offshore wind energy, the wake deficit minimization has become a critical challenge, particularly for offshore wind farms located in close proximity. Wake steering through active yaw control offers an effective means of maximizing the collective power output of offshore wind farm clusters. This paper proposes a cooperative active yaw control strategy that coordinately adjusts the yaw angles of the wind turbines to deflect their wake influencing regions in an offshore wind farm cluster. First, the mathematical expression of a three-dimensional yawed wake model is presented and a multiple wake synthesis method is applied. Second, a cooperative active yaw control model for offshore wind farm cluster power maximization is developed and the improved particle swarm genetic algorithm is introduced to solve it. Finally, the proposed method is applied to the simulation of an actual operating offshore wind farm cluster under two typical wind conditions as well as through real-time wake steering optimization on two representative days. Simulation results of the Qidong offshore wind farm cluster demonstrate that the proposed method can effectively mitigate both inter-farm and inner-array wake effects, leading to significant improvements in overall cluster power generation. Besides, the improved particle swarm genetic algorithm outperforms the genetic algorithm and the particle swarm optimization algorithm in solving the proposed model with superior stability and computational efficiency.