基于改进二进制量子粒子群算法的核心骨干网架搜索  

Core Backbone Network Searching Based on Improved Quantum Binary Particle Swarm Optimization

作　　者：王浩磊[1] 刘涤尘[1] 吴军[1] 潘旭东[1] 董飞飞[1] 赵一婕[1] 宋春丽[1] 

机构地区：[1]武汉大学电气工程学院,湖北省武汉市430072

出　　处：《中国电机工程学报》2014年第34期6127-6133,共7页Proceedings of the CSEE

基　　金：国家电网公司大电网专项项目(SGCC-MPLG029-2012);国家自然科学基金项目(51207114)~~

摘　　要：针对差异化规划设计提出的构建核心骨干网架,提高电网对重要负荷保障能力的要求,引入具有全局搜索能力的二进制量子粒子群算法。对于算法的缺陷和搜索模型的特性,通过引入小生境技术和遗传算法的交叉操作来增加算法的全局搜索能力,并运用图论修复粒子来提高算法效率。将改进的粒子群算法应用于IEEE-118模型的核心骨干网架搜索。仿真结果表明,该方法能有效地搜索出满足潮流约束要求和连通性要求的核心骨干网架。To meet the requirements of building a core backbone network and raise the ability to support important loads, which was presented by the differentiate planning and design, a global searching algorithm called quantum binary particle swarm optimization （QBPSO） was proposed in this paper on the basis of the features of searching model. The niche technique and the crossover operation of genetic algorithm were introduced to increase the global searching ability of the QBPSO. At the same time, the graph repair strategy was used to improve the efficiency of the QBPSO and the improved QBPSO was applied to core backbone network searching in IEEE-118 model. Simulation results show that this method can effectively search out the core backbone network, which can meet the requirements of flow restraint and connectivity.

关 键 词：电力系统 核心骨干网架 人工智能算法 连通性 图论 

分 类 号：TM76[电气工程—电力系统及自动化]