Performance enhancement of photovoltaic system integrated with a single-phase grid using advanced controllers

Abstract

This study offers a thorough examination of a photovoltaic (PV) system using a variety of maximum power point tracking (MPPT) methods, including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), perturb and observe (P&O), and artificial neural networks (ANN). Optimizing power extraction from PV systems under various environmental circumstances, including temperature variations and irradiance, is the main goal of these MPPT algorithms. Despite its widespread use and affordability, the P&O algorithm may have performance issues in dynamic circumstances. By using fuzzy logic to adjust to non-linear changes in environmental conditions, FLC improves P&O and offers more dependable and seamless operation. Although they demand a large amount of data and processing power, ANN-based MPPT approaches provide sophisticated capabilities by predicting optimal operating points by learning from historical system actions. By fusing fuzzy logic and neural networks, ANFIS offers a reliable solution that can more accurately adjust in real time to changing circumstances. These algorithms' incorporation into a PV system allows for more flexible and effective power management, guaranteeing peak performance in a range of climatic conditions. By combining many MPPT techniques, hybrid approaches can further reduce the drawbacks of individual approaches and improve the overall dependability and efficiency of PV systems.