摘要
采用导向搜索算法(OSA),以实现电力系统的无功优化.该算法将搜索个体模拟为人的搜索行为,搜索对象(目标函数最优解)模拟为可向搜索个体传送导向信息的智能体,以使搜索个体和搜索对象间可相互通讯.为验证该算法的有效性,以IEEE 57和IEEE 118节点测试系统为例进行了仿真,并与全面学习粒子群优化算法(CLPSO)和自适应遗传算法(AGA)的结果进行了比较.结果表明:导向搜索算法能得到高质量的全局最优解,IEEE 57和IEEE 118系统优化后的网损可分别减小13.871%和13.223%.
The oriented search algorithm(OSA) was used to optimize reactive power flow in a power system.In the OSA,the search-individual simulates human behavior,and the search-object(the optimal solution of the objective function) works like an intelligent agent that can transmit oriented information to search-individuals,so that search-individuals and the search-object can communicate with each other.The OSA was tested on IEEE 57-bus and IEEE 118-bus power systems in order to verify its efficiency,and the numerical results were compared with the ones obtained by the comprehensive learning particle swarm optimizer(CLPSO) and the adaptive genetic algorithm(AGA).The research results show that compared with the CLPSO and the AGA,the OSA can find high-quality optimal solutions,and the active power losses for optimized IEEE 57-bus and IEEE 118-bus power systems are decreased by 13.871% and 13.223%,respectively.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2010年第3期418-423,共6页
Journal of Southwest Jiaotong University
基金
国家自然科学基金资助项目(60870004)
关键词
导向搜索算法
无功优化
电力系统
oriented search algorithm
reactive power optimization
power system