Hybrid Intelligent Control and Maximum Power Point Tracking of a Solar Generator under Variable Irradiance and Temperature using a Multi-method Approach

This study presents a hybrid intelligent algorithm for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems, combining dynamic programming, Perturb and Observe (P&O), adaptive quick sort, and the Newton–Raphson method for solving nonlinear equations. The objective is to enhance tracking precision, reduce response time, and ensure stability under varying irradiance and temperature conditions. The system is modeled using an equivalent circuit of the PV cell, a boost-type DC-DC converter, and state-space representation for dynamic analysis. Simulations were performed in MATLAB under five different environmental scenarios, and results were validated against experimental measurements. Under standard test conditions (G = 1000 W/m², T = 25°C), the PV panel achieved a short-circuit current of 5.25 A, open-circuit voltage of 45 V, maximum power output of 180.82 W, fill factor of 0.765, and energy conversion efficiency of 15.2%. Across all test scenarios, the relative error remained below 0.0005, and MPPT convergence time was consistently under 15 milliseconds. A 100 MW solar generator comprising 22,811,520 cells and 380,192 panels was also evaluated, yielding power outputs between 13.27 MW and 101 MW with similarly negligible relative errors. Output voltage stabilized in under 35 milliseconds under all conditions, while efficiency ranged from 11.42% to 17.23%. The results confirm the algorithm’s high accuracy, fast convergence, and robust performance across variable environmental and power-scale conditions. These attributes make it a reliable and scalable solution for industrial PV systems and smart energy grids.