基于改进PSO算法的光伏阵列MPPT研究  

Research on MPPT of photovoltaic array based on improved PSO algorithm

作　　者：商立群[1] 闵鹏波 张建涛 SHANG Liqun;MIN Pengbo;ZHANG Jiantao(School of Electrical and Control,Xi’an University of Science and Technology,Xi’an 710000,China)

机构地区：[1]西安科技大学电气与控制工程学院,陕西西安710000

出　　处：《传感器与微系统》2024年第8期35-39,共5页Transducer and Microsystem Technologies

基　　金：陕西省自然科学基金资助项目(2021JM-393)。

摘　　要：为解决传统粒子群优化(PSO)算法在寻优过程中出现粒子早熟、收敛速度慢、易陷入局部优化等问题,提出一种基于反向学习的Logistic-Tent双重混沌映射和时变双重压缩因子(TVCF)策略的改进粒子群优化(LT-TVCFPSO)算法,在传统PSO算法基础上,引入了Logistic-Tent混沌映射和TVCF,既可增强种群多样性,避免粒子早熟,跳出局部优化,又能加快粒子收敛,提升全局寻优能力。最后在MATLAB/Simu-link上进行仿真。仿真结果表明:相比于传统MPPT算法,LT-TVCFPSO算法能够快速准确地追踪到全局最大功率点(GMPP)。In order to solve the problems of particle maturity,slow convergence speed,and easy to fall into local optimization in the traditional particle swarm optimization(PSO)algorithm in the process of optimizing,an improved PSO algorithm which based on reverse learning of logistic-Tent double chaotic mapping and time varying double constrict factor(TVCF)strategy(LT-TVCFPSO)is proposed,on the basis of traditional PSO algorithm,Logistic-Tent chaotic mapping and TVCF are introduced,which can not only enhance the diversity of the population,avoid particle prematurity,and jump out of local optimization,but also speed up the convergence of particle and improve the global optimizing ability.Finally,simulation is carried out by MATLAB/Simulink.Simulation results show that compared with the traditional MPPT algorithm,the LT-TVCFPSO algorithm can quickly and accurately track to the global maximum power point(GMPP).

关 键 词：全局寻优 改进粒子群优化算法 双重混沌映射 时变双重压缩因子 全局最大功率点 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]