最大功率点跟踪
粒子群优化
控制理论(社会学)
光伏系统
人口
群体行为
MATLAB语言
最大功率原理
算法
计算机科学
数学优化
全局优化
数学
功率(物理)
工程类
控制(管理)
逆变器
人工智能
物理
量子力学
电气工程
人口学
社会学
操作系统
作者
Xianqi Li,Ye He,Maojun Li
出处
期刊:Energies
[MDPI AG]
日期:2024-06-17
卷期号:17 (12): 2985-2985
被引量:2
摘要
In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.
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