自适应网格密度改进粒子群算法在多目标无功优化中的应用

摘要

粒子群算法因其具有收敛速度快和参数调节灵活等优点而被广泛应用于多目标无功优化领域,然而粒子群算法快速收敛特征极易收敛到局部最优解.提出了一种基于自适应网格密度的改进多目标粒子群算法(AG-MOPSO).通过自适应网格确定粒子的个体密度,并利用该信息对外部档案进行维护,进一步利用自适应网格估算非劣解的密度信息,采用轮盘赌法确定全局最优解,提升了种群多样性.以网损和电压偏移为目标,采用IEEE30节点系统进行算例分析,验证了改进AG-MOPSO算法在寻优过程中的高效性.%Particle swarm optimization (PSO) algorithm is widely applied to multi-objective reactive power optimization due to its fast convergence,flexible parameter adjustment and so on.However,the fast convergence characteristic of particle swarm algorithm is extremely easy to converge to local optimal solutions.To solve the problem,based on adaptive grid density,an improved multi-objective particle swarm algorithm (AG-MOPSO) is proposed in this paper.Individual density of each particle is determined by means of adaptive mesh,and its information is used to maintain the external file.The non inferior solution density information is estimated and combined with the roulette gambling law to determine the global optimal solution,which enhances population diversity.With the network loss and voltage offset as the targets,the IEEE30 node system is used to test and calculate the calculation examples,and the results verify the efficiency of the improved AG-MOPSO algorithm in the optimization process.