RESEARCH ON MAXIMUM POWER POINT TRACKING ALGORITHM OF PV ARRAY UNDER LOCAL SHADOW

被引：0
|
作者：
Xie B. [1 ]
Li P. [1 ]
Su Y. [1 ]
Su J. [1 ]
Liu T. [2 ]
机构：
[1] Hefei University of Technology, Research Center for Photovoltaic System Engineering of Ministry of Education, Hefei
[2] State Grid Anhui Ultra High Voltage Company, Hefei
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 12期
关键词：
grey wolf algorithm; improved artificial bee colony algorithm; maximum power point tracing; particle swarm optimization; PV modules;
D O I：
10.19912/j.0254-0096.tynxb.2022-1363
中图分类号：
学科分类号：
摘要：
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. © 2023 Science Press. All rights reserved.
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页码：47 / 52
页数：5
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