基于自适应人工蛙跳觅食算法的最优潮流计算方法

Optimal Power Flow Calculation Method Based on Adaptive Artificial Frog Jumping Foraging Algorithm

随着电力系统的快速发展和复杂性日益增加,最优潮流(Optimal Power Flow,OPF)计算作为电力系统分析的关键环节,对于提高电网的运行效率和可靠性具有重要意义。文章提出了一种基于自适应人工蛙跳觅食算法的最优潮流计算方法,旨在解决传统最优潮流计算方法在处理大规模非线性问题时的不足。为了解决算法在处理复杂电力系统问题时存在收敛速度慢和易陷入局部最优的问题,文章引入自适应策略,通过动态调整算法参数,提高算法的全局搜索能力和收敛速度。仿真实验结果表明,所提出的方法在解的质量、收敛速度和算法稳定性方面均表现出显著的优势。

With the rapid development and incrcasing complexity of the power system,Optimal Power Flow(OPF)calculation,as a key link in power system analysis,is of great significance for improving the operational efficicncy and reliability of the power grid.This article proposes an optimal power flow calculation mcthod based on adaptive artificial frog lcaping foraging algorithm,aiming to solve the shortcomings of traditional optimal power flow calculation methods in dealing with large-scale nonlinear problems.In order to solve the problems of slow convergence speed and casy getting stuck in local optima when dealing with complex power system problems,this paper introduces an adaptive strategy,which dynamically adjusts the algorithm parameters to improve the global search ability and convergence speed of the algorithm.The simulation experiment results show that the proposed method exhibits significant advantages in solution quality,convergence speed,and algorithm stability.