基于改进蜜蜂进化型遗传算法的含分布式电源的配电网重构  

Distribution network reconfiguration with distributed generation based on an improved bee evolutionary genetic algorithm

作　　者：王超学[1] 吕志奇[1] 董慧[1] 崔杜武[2] 孙有田[1] 

机构地区：[1]西安建筑科技大学信息与控制工程学院,陕西西安710055 [2]西安理工大学计算机科学与工程学院,陕西西安710048

出　　处：《电力系统保护与控制》2012年第15期52-56,62,共6页Power System Protection and Control

基　　金：国家自然科学基金(31170393);陕西省自然科学基金(2012JM8023);陕西省教育厅自然科学基金专项(12JK0726)~~

摘　　要：为改善分布式电源(Distributed Generation,DG)并入电网后配电网重构算法的性能,提出一种基于佳点集的蜜蜂进化型遗传算法(Bee Evolutionary Genetic Algorithm Based on Good Point Set,GBEGA)。该算法的关键有三点:1.提出一种基于佳点集的种群初始化方法,该方法比随机方法产生的种群在搜索空间更为均匀;2.引进佳点集交叉算子,该算子能在父代附近进行更加精细的搜索;3.采用自适应的交叉变异概率,有利于算法开采与勘探的平衡。将DG处理为PQ、PV两种模型,并将GBEGA与相关文献中的算法关于IEEE33和IEEE69节点系统进行了对比测试。仿真结果表明,GBEGA适合于含DG的配电网重构,在全局寻优能力和收敛速度上表现出色。To improve the performance of distribution network reconstruction algorithm with distributed generation （DG）, a new bee evolutionary genetic algorithm based on good point set （GBEGA） is proposed. The keys to GBEGA lies in three points as follows： firstly, proposing a population initialization method based on good point set, by which the distributing of initial population is more even in search space; secondly, introducing a crossover operator based on good point set, which has more elaborate search ability in the neighborhood of parent individuals; thirdly, adopting the self-adaption of crossover and mutation probability, which is conducive to balancing the exploration and exploitation capabilities of algorithm. Distributed generation is treated as PQ model and PV model, and control tests on IEEE 33 and IEEE 69 bus distribution systems are done between GBEGA and algorithms in pertinent literatures. The simulation results show that GBEGA can efficiently solves distribution network reconstruction problem with distributed generation and promises competitive performance not only in the convergence speed but also in the quality of solution.

关 键 词：分布式电源 配电网重构 蜜蜂进化型遗传算法 佳点集 自适应 

分 类 号：TM72[电气工程—电力系统及自动化]