Advanced Maximum Power Point Tracking in Photovoltaic Systems: A Comprehensive Review of Classical, AI-Based, and Metaheuristic Optimization Techniques

Abstract

Photovoltaic (PV) systems play a vital role in harnessing solar energy, which has become increasingly important due to growing environmental concerns and the pressing demand for renewable energy sources (RES). Maximizing the efficiency and enhancing the performance of PV systems heavily depend on effective optimization techniques, particularly those based on Maximum Power Point Tracking (MPPT). This paper offers a thorough review of various MPPT methodologies, emphasizing their respective contributions to enhancing power extraction in PV systems. Additionally, it explores the intersection of innovation with emerging technologies such as artificial intelligence and metaheuristic optimization in advancing MPPT techniques. The study underscores the pivotal role of MPPT in enhancing PV system efficiency and identifies emerging trends in AI-based techniques and metaheuristic optimization algorithms. The findings demonstrate the exceptional accuracy and flexibility of these strategies in monitoring the elusive Maximum Power Point (MPP) under changing environmental conditions. Moreover, the integration of metaheuristic optimization (MO) based MPPT methods is shown to effectively address the inherent challenges associated with conventional approaches. The paper concludes by emphasizing the potential of metaheuristic algorithms to traverse the intricate and non-linear attributes of PV systems, enabling the extraction of the highest possible power output across different environmental situations.