An Active Wake Control Strategy for Tandem Wind Turbines Based on Multi-Attractor Particle Swarm Optimization Algorithm

Active wake control can improve the wake distribution within wind farm and enhance power output. The Particle Swarm Optimization (PSO) algorithm is commonly employed to address wake optimization problems. However, its performance is suboptimal when the population size is small, despite the advantage of a shorter solution time. This paper proposes a Multi-Attractor Particle Swarm Optimization algorithm, which allows particles to track different attractors with higher fitness rather than the historical best of the population, thereby enhancing the global search capability of the algorithm. The result shows that the proposed algorithm is not prone to premature convergence and still has outstanding performance even with a small population size,providing a rapid solution for the wake optimization problem in large-scale wind farms. Furthermore, this paper investigates the impacts of output power of the first wind turbine, incoming wind speed, and turbine spacing on the active power optimization. The result indicates that the optimization of output power of whole wind farm can be hindered due to excessive power loss of the first wind turbine. In addition, the performance of optimization is significantly affected by the spacing between turbines with a demonstrated negative correlation, while the impact of wind speed is relatively minor.