A Fast Optimization Algorithm for Wind Farm Wake Control

Active wake control technology can effectively mitigate the wake effect in large wind farms, thereby enhancing the overall power generation. Axial induction control reduces the energy capture rate of upstream turbines by lowering their axial induction factors, allowing downstream turbines to capture more energy from the wake, ultimately improving the overall energy capture rate of the wind farm. Mathematical algorithms are typically employed to determine the optimal axial induction factors for operating wind turbines, but the computation time is lengthy, making it difficult to meet the real-time control requirements of wind farms. To address this issue, this paper proposes a wind farm wake control method using a fast optimization algorithm. First, the Horns Rev offshore wind farm is modeled, followed by the introduction of a rapid calculation method for axial induction control. Finally, the results of the fast optimization algorithm are compared and analyzed with those obtained using traditional continuous optimization algorithms, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The conclusion is that the new optimization algorithm significantly reduces the computation time while achieving a good sub-optimal solution.