Siddique Muhammad Abu Bakar, Zhao Dongya, Ouahada Khmaies, Rehman Ateeq Ur, Hamam Habib
College of New Energy, China University of Petroleum (East China), Qingdao, 266580, China.
Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa.
Sci Rep. 2025 May 16;15(1):17061. doi: 10.1038/s41598-025-01816-3.
The photovoltaic (PV) energy is essential for the future of sustainable energy developments. Conventional algorithms perform well in maximum power extraction under uniform irradiance conditions (UIC). However, they often struggle to maintain the global maximum power point (GMPP) under simple partial shading conditions (SPSCs), frequently getting stuck at local maximum power points (LMPPs) and resulting in power loss. This study developed an adapted perturb and observe based model predictive control (APO-MPC) maximum power point tracking (MPPT) approach in MATLAB/Simulink, comprising six series-connected PV modules, a boost converter, and load. The control strategy identifies GMPP and computes reference current to minimize the cost function of an optimization problem. It was compared with other MPPT algorithms regarding tracking accuracy, convergence speed, computational time, steady-state oscillations (SSOs), power efficiency under UIC, SPSCs, and complex partial shading conditions (CPSCs). The system was validated using real-time hardware implementation and seasonal field atmospheric data. The results indicated that the APO-MPC algorithm outperformed the others with no oscillations during GMPP tracking, average convergence time, and tracking efficiency of 0.17 s and 99.46%, respectively. The findings confirm its highly fast, accurate, and stable tracking of GMPP without getting trapped into LMPPs under CPSCs.
光伏（PV）能源对于可持续能源发展的未来至关重要。传统算法在均匀辐照条件（UIC）下的最大功率提取方面表现良好。然而，在简单部分阴影条件（SPSC）下，它们往往难以维持全局最大功率点（GMPP），经常被困在局部最大功率点（LMPP），从而导致功率损失。本研究在MATLAB/Simulink中开发了一种基于改进的扰动观察法的模型预测控制（APO-MPC）最大功率点跟踪（MPPT）方法，该方法由六个串联的光伏模块、一个升压转换器和负载组成。该控制策略识别GMPP并计算参考电流，以最小化优化问题的成本函数。在跟踪精度、收敛速度、计算时间、稳态振荡（SSO）、UIC、SPSC和复杂部分阴影条件（CPSC）下的功率效率方面，将其与其他MPPT算法进行了比较。该系统通过实时硬件实现和季节性现场大气数据进行了验证。结果表明，APO-MPC算法在GMPP跟踪过程中无振荡，平均收敛时间为0.17 s，跟踪效率为99.46%，优于其他算法。研究结果证实了其在CPSC下能够高度快速、准确且稳定地跟踪GMPP，而不会陷入LMPP。
