基于CPSO和DE改进粒子群算法的无功优化仿真

发布时间：2018-03-17 11:21

  本文选题：粒子群　切入点：混沌粒子群　出处：《实验室研究与探索》2016年10期 　论文类型：期刊论文

【摘要】：传统的粒子群优化(Particle Swarm Optimization,PSO)算法易陷入局部最优,因此引入了混沌优化形成混沌粒子群(Chaotic Particle Swarm Optimization,CPSO)算法以减小粒子陷入局部最优的可能,并在此基础上结合了差异进化(Differential Evolution,DE)算法中的交叉操作得到改进粒子群优化(Improved Particle Swarm Optimization,IPSO)算法以增加粒子的多样性,从而增加获得更优解的可能。为验证算法有效性,将PSO、CPSO和IPSO基于Matlab软件分别对IEEE30节点测试系统进行电力系统无功优化仿真。仿真结果表明,IPSO算法能找到质量更高的解,且收敛特性更好,体现了算法改进的优越性。通过该仿真实验,既可加强学生运用仿真软件的能力,又可加深学生对无功优化的理解和对智能算法的认识,从而有效提高教学质量。
[Abstract]:The traditional particle swarm optimization (PSO) algorithm is easy to fall into local optimum, so chaotic Particle Swarm optimization algorithm is introduced to reduce the possibility of particle falling into local optimization. On the basis of this, the improved Particle Swarm optimization (IPSOs) algorithm is obtained by combining the cross operation in the differential evolution evolution (DEE) algorithm to increase the diversity of particles and increase the possibility of obtaining a better solution. In order to verify the effectiveness of the algorithm, the improved particle swarm optimization (PSO) algorithm is proposed in this paper. The reactive power optimization simulation of IEEE30 node testing system based on Matlab software is carried out by PSO-CPSO and IPSO respectively. The simulation results show that the PSO-CPSO algorithm can find a higher quality solution and the convergence characteristic is better. The simulation experiment can not only strengthen the students' ability to use the simulation software, but also deepen the students' understanding of reactive power optimization and the understanding of the intelligent algorithm, so as to improve the teaching quality effectively.
【作者单位】： 重庆邮电大学自动化学院;重庆市教育科学研究院高等教育研究所;
【基金】：重庆市研究生教改项目(yig143061) 重庆邮电大学教育教学改革项目(XJG1522)
【分类号】：TP18
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本文编号：1624514