A photovoltaic generator (PVG) is a sophisticated integration of multiple photovoltaic (PV) cells designed to harness solar energy efficiently. In essence, it serves as a pivotal component in solar energy systems, converting sunlight into electricity. The power output of a PV module is intricately tied to various environmental and electrical factors, such as irradiation levels and ambient temperature. These factors contribute to the fluctuations observed in the maximum Power Point (MPP), which is the optimal operating point of the photovoltaic generator (PVG), representing the point at which the generator achieves its highest efficiency and power output.

Efficiently extracting the maximum energy from a PV system requires precise control mechanisms, and one of the key strategies employed for this purpose is Maximum Power Point Tracking (MPPT). MPPT algorithms play a crucial role in continuously adjusting the operating point of the PVG to ensure that it operates at or near its MPP under varying environmental conditions.

The primary focus of this study is to develop and evaluate control mechanisms aimed at consistently extracting the maximum energy from the PV system. Specifically, attention is directed towards enhancing the performance of the DC/DC converter and the PV generator within the system. To achieve this objective, a comparative analysis of various MPPT algorithms is conducted. The algorithms under investigation include Incremental Conductance (INC), a Fuzzy Logic Controller (FLC), Particle Swarm Optimization (P&O), and Neural Networks (ANNs).

Through extensive simulations performed using the MATLAB Simulink software, the efficacy and robustness of each MPPT algorithm are thoroughly assessed. The simulation results provide valuable insights into the performance of the proposed system under different operating conditions and help identify the most suitable MPPT algorithm for maximizing energy extraction from the PV system.