Investigation of Artificial Intelligence Algorithms for MPPT of Solar Photovoltaic System

Abstract

Solar energy has gained prominence as a primary renewable energy source for the generation of electricity in recent years. The maximization of power extraction from photovoltaic (PV) systems is a topic of significant interest due to the relatively low conversion efficiency of these systems. Therefore, a maximum power point tracking (MPPT) controller is essential in a PV system to achieve the desired output power. This paper implements three different MPPT controllers: sliding mode control (SMC), fuzzy logic controller (FLC), and artificial neural networks (ANNs). The performance of these controllers is evaluated on a PV system under varying irradiation and temperature conditions to analyze their ability to track the maximum power point (MPP). The results demonstrate that the SMC outperforms the FLC and ANN in terms of best performance with minimum oscillation under different operating conditions.