多目标粒子群优化算法在光伏MPPT中的应用

Application of Multi-objective Particle Swarm Optimization Algorithms in MPPT Control Method of PV System

为提高光伏电池的利用率,需要进行光伏阵列的最大功率点跟踪(MPPT),针对传统粒子群优化算法在多目标优化中的不足,提出了基于最小粒子角度的多目标粒子群优化算法,利用目标空间中不同粒子之间的角度进行粒子全局极值更新,通过比较粒子的浓度值给出粒子群及粒子个体极值更新方法,并在Matlab/Simulink下进行了建模与仿真。仿真结果显示,该算法在外界环境变化时能快速准确地跟踪太阳能电池的最大功率点,并能保证系统的稳定性。

In order to improve the utilization rate of photovoltaic cells,it is necessary to track the maximum power point of photovoltaic array.Aiming at shortages of traditional PSO algorithm for multi-objective optimization,multi-objective PSO algorithm based on minimal particle angles is proposed.The global optimal particle is updated by comparison of angles among different particles in objective space.The method of updating local optimal particle and swarm is presented based on comparison of particle densities.The maximum power point tracking method is established and simulated with Matlab/Simulink.Simulation results show that the algorithm can rapidly and accurately track the maximum power point when the external environment changes and it ensures the stability of PV system.