基于混合粒子群优化算法的电力系统无功优化

应用粒子群优化算法(PSO)求解电力系统无功优化问题,提出基于混沌搜索的混合粒子群优化算法,以克服PSO容易早熟而陷入局部最优解的缺点。该算法引入了基于群体适应度方差的早熟判断机制,当算法陷入早熟时,利用混沌运动的遍历性、随机性和规律性等特性,先对当前粒子群体中的最优粒子进行混沌寻优,然后把混沌寻优的结果随机替换群体中的一个粒子,从而提高了PSO的寻优特性。通过对IEEE 14、IEEE 30、IEEE 118等标准测试系统进行无功优化,并与遗传算法、标准PSO进行比较,表明该算法具有更高的搜索效率和更好的全局优化能力。

Reactive Power Optimization Based on Hybrid Particle Swarm Optimization Algorithm

The chaos search based hybrid particle swarm optimization(PSO) algorithm is proposed in the paper to avoid the premature phenomenon of PSO,which is applied into the reactive power optimization.The mechanism for judging local convergence is introduced based on variance of population's fitness.Taking advantage of the ergodicity,randomicity and regularity of chaotic movement,a new global superior individual is obtained by chaotic search,by which an individual in the current population is randomly replaced,when the process appears local convergence.Case study on IEEE 14-bus,IEEE 30-bus and IEEE 118-bus proves that the proposed algorithm has higher search efficiency and better capability of global optimization than the genetic algorithm and standard PSO.