基于动态多种群粒子群算法的无功优化

提出了一种基于动态多种群策略的改进粒子群算法。该算法将传统粒子群优化算法(particle swarm optimization，PSO)中的种群划分成多个子群，每个子群相对独立地朝同一目标进化，仅通过一种轮形结构的弱联系进行交流。在进化过程中各种群不断分裂和聚类重组，动态调整种群规模以更好地适应进化。该算法可以较好地避免PSO算法过快收敛于局部最优解，并且有较快的收敛速度。文中将该算法应用于求解电力系统无功优化问题，并与标准PSO算法的性能进行了对比，仿真计算证明该算法是有效、可行的。

Application of Modified Particle Swarm Optimization in Reactive Power Optimization

A dynamic multi-group strategy based modified particle sward optimization(PSO) algorithm is proposed,in which the groups in traditional PSO are re-divided into multi subgroups and each subgroup evolutes towards the same target relatively independently,these subgroups share information by means of a weak relationship with a wheel structure.During the evolution,various groups unceasingly split and recombine by clustering,and dynamically the sizes of groups are dynamically adjusted to fit the evolution better.The convergence speed of the proposed algorithm is high and by use of the proposed algorithm the local optimal solution of PSO algorithm can be avoided.In this paper the proposed algorithm is applied to solve the reactive power optimization of power system and compared with the results by standard PSO algorithm.Simulation results show that the proposed modified PSO algorithm is effective and feasible.