基于强引导粒子群与混沌优化的电力系统无功优化

为解决粒子群优化后期搜索速度较缓慢,易陷入局部最优的问题,提出一种基于强引导粒子群与混沌寻优相结合的电力系统无功优化算法,该算法在采用强引导型粒子群的基础上引入混沌优化以进一步提高全局寻优能力,即在粒子群算法的基础上引入强引导思想,在搜索初期,对粒子位置的更新加以引导,减少算法随机性以提高搜索效率。为进一步解决寻优后期粒子可能陷入早熟收敛的问题,利用混沌优化具有"奇异吸引子"的特性,在解空间进一步搜索,两者的结合可以更有效地搜索到全局最优解。通过对某高压配电网的具体计算,最优降损率可以达到14.04%,节点最低电压从0.895 0 p.u.提高到0.995 6 p.u.,结果表明该算法应用在电力系统无功优化领域的可行性和有效性。

Reactive power optimization based on induction-enhanced particle swarm optimization and chaos search

An algorithm based on induction-enhanced particle swarm optimization and chaos search is proposed for reactive power optimization to solve the problems of traditional PSO(Particle Swarm Optimization),such as local optimum and slow convergence,and improve the ability of global optimization.During the early search,the induction is introduced to the update of particle position to decrease the randomieity of PSO and improve the search efficiency,while during the late search,the strange attractor character of chaos is applied to effectively avoid the premature of PSO and find the global optimal solution.Calculations for a power distribution system show that,the optimal loss-lowering rate is 14.04 %,and the minimum voltage is increased from 0.895 0 p.u.to 0.995 6 p.u.,validating the feasibility and effectiveness of the proposed algorithm.