Assessment of Adaptive Neural-Fuzzy MPPT in Photovoltaic Grid Connected System

In order to maximise the efficiency of a 3 MW grid-connected photovoltaic (PV) system, this study provides a thorough performance evaluation of the Adaptive Neuro-Fuzzy Inference System (ANFIS), Fuzzy Logic Controller (FLC), and Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) methodologies. The study focusses on each MPPT method's power transfer capabilities, steady-state stability, and dynamic response under various temperature and irradiance conditions. According to simulation results, with an output of 2.75 MW, the ANFIS-based MPPT ensures effective power injection into the grid by achieving the fastest convergence to the Maximum Power Point (MPP) and stabilising in 0.01 seconds with few oscillations. With stabilisation within 0.05 seconds and little steady-state oscillations, the FLC-based MPPT demonstrates strong tracking ability. The P&O-based MPPT, on the other hand, exhibits slower convergence and prolonged oscillations, which results in higher power losses and less than ideal energy extraction. The comparison research highlights how ANFIS and FLC-based MPPT techniques outperform the traditional P&O strategy in terms of tracking efficiency, response time, and stability, which makes them ideal for grid-connected PV applications in dynamic environmental conditions.