基于粒子群与微分进化算法的无功优化

Power System Reactive Power Optimization Based on Particle Swarm Optimization and Differential Evolution Algorithm

应用传统粒子群算法(PSO)于电力系统无功优化问题存在收敛精度不高、陷入局部最优的缺点,利用微分进化算法(DE)的随机变异性,将当前所产生的局部最优值进行变异,再重回PSO搜寻全局最优值,从而提高了PSO算法的寻优特性,应用于IEEE30节点,验证所提算法是可行和有效的。

The particle swarm optimization is applied into the reactive power optimization, which is inadequate and insufficient to the optimal operation of power systems due to the inherent complexity. Taking advantage of the randomicity, variability of differential evolution, making the local superior individual of PSO mutated, then returning to PSO to serach the global superior individual, the ability of the search has been improved, the propsed algorithm is tested on IEEE30-bus, and the results verify the feasibility and effectiveness of the proposed algorithm.