Wake-Aware Online Cluster Optimization for Flexible Wind Farm Yaw Control

Abstract

The integration of wind energy into power systems is expanding rapidly, bringing with it the need for optimized wind farm operations to maximize power output and efficiency. This paper addresses the critical challenge of yaw control in wind farms, which involves adjusting turbine orientation to optimize alignment with wind direction while accounting for complex wake effects that can significantly impact downstream turbines. To tackle these issues, this paper proposed an online cluster optimization algorithm designed for real-time adaptation to changing wind conditions. This algorithm features multi-objective optimization to balance the maximization of power output against the operational costs associated with yaw adjustments, including potential mechanical wear. Through case studies, the proposed method demonstrates its ability to enhance the overall performance of wind farms, achieving a 15.24% increase in power generation compared to the baseline greedy strategy, by mitigating wake effects and optimizing yaw angles under dynamic operational constraints.