摘要
传统粒子群算法在求解分布式电源的优化配置问题时,存在早熟收敛、易陷入局部最优的现象。为解决这些问题,提出一种基于传统粒子群算法的人工免疫粒子群算法。该算法把人工免疫系统的多样性、免疫记忆特性与传统粒子群算法有机结合,提高了算法的全局搜索能力,保留了高适应度的个体,解决了传统算法早熟收敛、局部搜索能力差的不足。通过对IEEE33节点配电测试系统进行仿真计算,验证了所提算法具有更好的搜索性能和寻优能力。
When the traditional particle swarm optimization algorithm solves the optimal allocation problem of distributed generation,the problem of premature convergence and local optimum has emerged. In order to solve these problems,this paper presented an artificial immune particle swarm optimization(AI-PSO) algorithm based on traditional particle swarm optimization algorithm. The algorithm combined the diversity and immune memory of artificial immune system with the traditional particle swarm optimization,which improved the global search ability of the algorithm,retained the high fitness individual,and solved the problem of premature convergence and poor local search ability of traditional algorithms. The simulation results of IEEE33 node distribution test system show that the proposed algorithm has better search performance and capability.
作者
曹申
粟世玮
曹文康
杨玄
CAO Shen;SU Shiwei;CAO Wenkang;YANG Xuan(Collage of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China)
出处
《电力科学与工程》
2019年第10期57-61,共5页
Electric Power Science and Engineering
关键词
分布式电源
优化配置
配电系统
人工免疫粒子群算法
寻优
distributed generation
optimal allocation
power distribution system
AI-PSO
optimization
