基于蜜蜂双种群进化型云自适应遗传算法的电力系统多目标无功优化  

Multi-objective reactive power optimization for power system based on double bee population evolutionary cloud adaptive genetic algorithm

作　　者：周海忠[1] 周步祥[1] 何春渝 周岐杰 彭章刚 王精卫 

机构地区：[1]四川大学电气信息学院,成都610065 [2]国网四川达州供电公司,四川达州635000

出　　处：《电测与仪表》2016年第5期103-108,共6页Electrical Measurement & Instrumentation

摘　　要：针对遗传算法在求解多目标无功优化方面存在的缺陷,文章提出了基于蜜蜂双种群进化型云自适应遗传算法(double bee population evolutionary cloud adaptive genetic algorithm,BEPE-CAGA)。该算法根据蜜蜂双种群进化思想,引入了雄峰通过竞争参与交叉及雄峰与决定双峰群优秀遗传基因的蜂后交叉的策略,并结合正态云模型云滴的随机性和稳定倾向性特点对其进行改进,改善了算法陷入早熟的问题,提高了算法的收敛速度。建立了以有功网损最小、电压偏差最小及电压稳定裕度最大为目标的无功优化数学模型,并以BEPE-CAGA算法求解该模型。最后通过对IEEE14和IEEE30节点系统进行算例仿真,仿真结果验证了文章所提算法的有效性,同时也证明了该算法在收敛速度和优化效果上具有比基本GA算法和CAGA算法更佳的性能。In this paper,a double bee population evolutionary cloud adaptive genetic algorithm（ BEPE-CAGA） is proposed to cope with the limitation of genetic algorithms in solving the multi-objective reactive power optimization.Based on double bee population evolutionary thought,after the introduction of competition to participate in a cross by drones and drones and decided doublets excellent cross bee genetic strategies,and then,in combination of the normal cloud model cloud droplet characteristics of randomness and stable tendency,this algorithm solved the problem of premature convergence of genetic algorithm and speeded up the convergence speed. This paper established a reactive power optimization mathematical model with minimum active power loss,voltage deviation minimum and maximum voltage stability margin goals,and BEPE-CAGA algorithm was used to solve the model. Finally,the examples simulation results of IEEE14 and IEEE30 node system verify the effectiveness of the proposed algorithm,as well as proved that the algorithm was better than the basic GA algorithm and CAGA algorithm performance on the convergence speed and optimization results.

关 键 词：蜜蜂双种群 云自适应 多目标 无功优化 遗传算法 

分 类 号：TM933[电气工程—电力电子与电力传动]