论文部分内容阅读
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 ratio of photovoltaic cells, maximum power point tracking (MPPT) of photovoltaic array is required. To overcome the shortcomings of traditional particle swarm optimization algorithm in multi-objective optimization, a multi-objective particle swarm optimization algorithm based on minimum particle angle is proposed. In this paper, we update the global maximum of particles by the angle between different particles in the target space. The particle swarm optimization and particle individual updating method are given by comparing the particle concentration values. The modeling and simulation are carried out under Matlab / Simulink. The simulation results show that the algorithm can track the maximum power point of the solar cell quickly and accurately when the external environment changes, and can ensure the stability of the system.