针对局部阴影下光伏阵列输出功率的多峰值问题,传统的MPPT跟踪算法不能准确跟踪系统的最大功率点,为此,该文研究了3种基于人工智能算法的光伏阵列MPPT算法,包括粒子群算法、灰狼算法和改进人工蜂群算法。该文详细介绍了3种人工智能算法的原理及算法流程,并在Matlab/Simulink中搭建系统的仿真模型,对比3种算法在静态阴影遮挡和阴影突变情况下的MPPT跟踪性能,结果表明：3种人工智能算法均能有效跟踪光伏阵列的最大功率点,跟踪误差均小于0.5%,其中粒子群算法跟踪精度最高,收敛速度最慢,而灰狼算法跟踪精度最低,收敛速度最快,在收敛稳定性方面,相较于灰狼算法和改进人工蜂群算法,粒子群算法更易陷入局部最优。
Abstract
Suffering from the multiple peaks of PV modules under local shadows,the traditional MPPT algorithms cannot accurately track their maximum power point. In the paper, three MPPT algorithms of PV modules based on artificial intelligence algorithms are studied, including particle swarm optimization algorithm, gray wolf algorithm, and improved artificial bee colony algorithm. This paper provides a detailed introduction to the principle and process of three artificial intelligence algorithms,and a simulation model of the system is established in Matlab/Simulink. By comparing the MPPT tracking performance of the three algorithms under static shadow occlusion and sudden shadow changes, the simulation results show that all three artificial intelligence algorithms can effectively track the maximum power point of PV modules, with the tracking errors less than 0.5%. Among them, particle swarm optimization algorithm has the highest tracking accuracy and the slowest convergence speed. The grey wolf algorithm has the lowest tracking accuracy and the fastest convergence speed. In terms of convergence stability, compared to the grey wolf algorithm and the improved artificial bee colony algorithm,the particle swarm optimization algorithm is more prone to track the local optima.
关键词
光伏组件 /
最大功率点跟踪 /
粒子群优化 /
灰狼算法 /
改进人工蜂群算法
Key words
PV modules /
maximum power point tracing /
particle swarm optimization /
grey wolf algorithm /
improved artificial bee colony algorithm
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基金
安徽省自然科学基金（2208085QE165）; 台达电力电子科教发展计划（DREG2022010）; 中央高校基本科研业务费专项资金
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