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 = ۱۰۰۰ W/m², T = ۲۵°C), the PV panel achieved a short-circuit current of ۵.۲۵ A, open-circuit voltage of ۴۵ V, maximum power output of ۱۸۰.۸۲ W, fill factor of ۰.۷۶۵, and energy conversion efficiency of ۱۵.۲%. Across all test scenarios, the relative error remained below ۰.۰۰۰۵, and MPPT convergence time was consistently under ۱۵ milliseconds. A ۱۰۰ MW solar generator comprising ۲۲,۸۱۱,۵۲۰ cells and ۳۸۰,۱۹۲ panels was also evaluated, yielding power outputs between ۱۳.۲۷ MW and ۱۰۱ MW with similarly negligible relative errors. Output voltage stabilized in under ۳۵ milliseconds under all conditions, while efficiency ranged from ۱۱.۴۲% to ۱۷.۲۳%. 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.

کلیدواژه ها:

Maximum power point tracking ، photovoltaic systems ، Hybrid Intelligent Control ، dynamic programming algorithm ، Environmental Condition Adaptability