Dynamic PI-PD cascaded MPPT controller for SPV system with battery charging circuit

Sweety Kumari, Ramesh Kumar
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Abstract

This paper proposes a dynamic maximum power point tracking (MPPT) controller for a solar photovoltaic (SPV) system with a battery charging circuit. The voltage and current, and consequently the maximum available power of SPV panels vary based on environmental conditions. To operate SPV system at maximum power point under different weather conditions, a cascaded (PI-PD) controller with PSO gain scheduling is suggested in this paper. Also, the FOPI control is applied to an accurate dynamic model of the buck converter to function as a charge controller. For tuning the FOPI controller parameters, a stochastic inertia weight GWO algorithm is employed which maintains an appropriate balance between detection and hunting strategies, and gives the fittest wolf position during iterations. The proposed algorithm is compared with the original GWO algorithm to show its superiority. The accuracy of the proposed cascaded controller used in the SPV system to find MPP ranges from 96.05% to 98.87%. The goal of this study is to operate the SPV panel at maximum power point under variable atmospheric conditions to increase efficiency at a lower cost. It also provides appropriate current and voltage for faster battery charging, thereby increasing the life span of the battery. The system is implemented and analyzed in MATLAB/Simulink, and results are validated.
带电池充电电路的 SPV 系统动态 PI-PD 级联 MPPT 控制器
本文为带有电池充电电路的太阳能光伏(SPV)系统提出了一种动态最大功率点跟踪(MPPT)控制器。SPV 电池板的电压和电流以及最大可用功率随环境条件而变化。为使 SPV 系统在不同天气条件下以最大功率点运行,本文提出了一种带有 PSO 增益调度的级联(PI-PD)控制器。此外,还将 FOPI 控制应用于降压转换器的精确动态模型,以发挥充电控制器的作用。为调整 FOPI 控制器参数,采用了随机惯性权重 GWO 算法,该算法在检测和捕猎策略之间保持适当平衡,并在迭代过程中给出最合适的狼位置。将所提出的算法与原始 GWO 算法进行了比较,以显示其优越性。在 SPV 系统中使用所提出的级联控制器找到 MPP 的准确率为 96.05% 至 98.87%。这项研究的目标是在多变的大气条件下以最大功率点运行 SPV 面板,从而以较低的成本提高效率。它还能提供适当的电流和电压,加快电池充电速度,从而延长电池的使用寿命。该系统在 MATLAB/Simulink 中实现和分析,并对结果进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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