基于蝙蝠与粒子群混合优化算法的光伏MPPT研究

Photovoltaic MPPT study based on bat and particle swarm hybrid optimization algorithm

在局部遮荫条件下,传统最大功率点追踪(MPPT)算法容易失效,群智能优化算法追踪时间较长。为此,提出了一种基于蝙蝠与粒子群混合优化(BPSHO)的MPPT算法。在算法的前期,采用蝙蝠算法;在算法中后期,采用粒子群优化算法。按照指数规律调节算法的参数,并在算法的中期加入局部搜索机制。仿真与实验结果表明:BPSHO算法能够准确地跟踪到最大功率点,跟踪时间只有粒子群优化算法的一半左右。

Under partial shade conditions,traditional maximum power point tracking(MPPT)algorithms are prone to failure,and swarm intelligence optimization algorithms have long tracking time.For this reason,an MPPT algorithm based on Bat and Particle Swarm Hybrid Optimization(BPSHO)was proposed.In the early stage of the algorithm,bat algorithm was used,and in the middle and late stages of the algorithm,particle swarm optimization algorithm was adopted.Parameters of the algorithm were adjusted according to exponential law,and added a local search mechanism in the middle of the algorithm.Simulation and experimental results show that the BPSHO algorithm can accurately track to maximumpower point,and the tracking time is only about half of particle swarm optimization algorithm.