CAJ | 학술논문

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。
침대차이화규화설계제출적구건핵심골간망가，제고전망대중요부하보장능력적요구，인입구유전국수색능력적이진제양자입자군산법。대우산법적결함화수색모형적특성，통과인입소생경기술화유전산법적교차조작래증가산법적전국수색능력，병운용도론수복입자래제고산법효솔。장개진적입자군산법응용우IEEE-118모형적핵심골간망가수색。방진결과표명，해방법능유효지수색출만족조류약속요구화련통성요구적핵심골간망가。
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.