基于混沌云遗传算法的配电网无功优化研究  

Research of Distribution Network Reactive Power Optimization Based on Chaos Cloud Genetic Algorithm

作　　者：张佩炯[1] 苏宏升[1] 景利学[1] 

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070

出　　处：《计算机测量与控制》2013年第2期505-508,共4页Computer Measurement &Control

基　　金：国家自然科学基金资助项目(61163009);甘肃省自然科学基金资助项目(2010GS04266)

摘　　要：针对传统遗传算法在配电网无功优化中收敛速度慢和搜索精度不高,且易出现早熟现象问题,提出了一种混沌云遗传算法(CCGA);该算法在云遗传选择、交叉、变异执行完一次后的基础上引入混沌思想,结合混沌运动的遍历性、随机性和规律性的特点,通过混沌移民操作增加了控制变量取值的多样性,从而克服传统GA中优化解落入局部最优和搜索精度不高的缺陷;将该算法分别应用到IEEE30节点配电网和甘肃玉门电网进行算例仿真验证,并与其它算法相比较;结果表明,该方法收敛速度快,效率高,搜索精度高,验证了算法的有效性和实用性。As traditional genetic algorithm has the disadvantage of slow convergence speed and early maturing in distribution network reactive power optimization, and search precision is low, a chaos cloud genetic algorithm （CCGA） is presented in this paper, This algorithm introduced chaos thought and combined with the characters of chaos motion, including ergodicity, randomicity and regularity, after the cloud genetic completed selection, crossover and mutation, and increased control variable the diversity through the chaotic immigration operating, so overcomed optimization solution trapped into local optimal and search precision is low in the traditional GA. The algorithm is respectively applied to IEEE30 node distribution network and GanSu YuMen grid power for simulation validation, and compared with other algorithms. The results show that the algorithm has fast convergence and high efficiency, high precision search, the algorithm＇s effective and practical is proved.

关 键 词：云遗传 云模型 混沌移民算子 无功优化 配电网 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]