基于改进多粒子群算法的电力系统无功优化

将改进的多粒子群算法应用于电力系统无功优化问题的求解，克服了传统粒子群算法收敛精度不高、易陷入局部最优的缺点．该优化方法对原粒子群算法进行了如下改进：通过增强粒子群间的协同作用、引入惯性因子以及扰动的策略，来平衡集中强化搜索和分散多样化搜索过程．对IEEE6节点和IEEE30节点系统分别进行无功优化计算，并与传统粒子群算法进行了比较，结果表明，该算法求得的有功损耗较原状态降低了近1／5，且电压合格率为100％，具有较强的全局搜索能力和较高的收敛精度，是求解无功优化的有效方法．

Reactive Power Optimization Based on Improved Poly-Particle Swarm Optimization Algorithm

An improved poly-particle swarm optimization algorithm(IPPSO) is proposed for reactive power optimization to overcome the disadvantages of lower degree of convergence and frequent trapping in local optimum in the traditional particle swarm algorithm.The new method enhances the cooperative interaction among each particle swarm,and adopts inertia weight and destabilizations,to balance the intensification strategy and diversification strategy.The proposed algorithm has been successfully applied to IEEE 6-bus and IEEE 30-bus system,and the comparative results show that the active loss reduces nearly by 1/5 and the voltage constraints are all satisfied,which verifies the better search capability and higher degree of convergence for reactive power optimization.