Performance Analysis of Adaptive Neural-Fuzzy MPPT in Photovoltaic System

This paper presents a comparative analysis of Maximum Power Point Tracking (MPPT) techniques for photovoltaic (PV) systems, focusing on the Adaptive Neural-Fuzzy Inference System (ANFIS)-based MPPT controller. The study evaluates ANFIS in comparison with traditional Perturb and Observe (P&O) and Fuzzy Logic Controller (FLC) methods, under both constant and varying solar irradiance conditions. Simulation results demonstrate that the ANFIS-based MPPT offers superior performance in terms of convergence speed and power extraction efficiency, achieving faster response times and reduced oscillations around the maximum power point. The proposed ANFIS method improves tracking accuracy by leveraging the hybrid benefits of neural networks and fuzzy logic, making it a robust solution for dynamic PV environments. This approach is tested using MATLAB/Simulink simulations on a 3 MW PV system. The results confirm the ANFIS controller's effectiveness in maximizing power output and reducing computational complexity compared to traditional methods.