Artificial Intelligence Based Maximum Power Point Tracking Algorithm for Solar Photovoltaic System

The Fractional Order based Artificial Recurrent Neural Network (FOARNN) has emerged as a critical tool for improving the effectiveness of Maximum Power Point Tracking (MPPT) algorithms for solar photovoltaic (PV) systems, especially under difficult conditions such as partial shadowing. When several peaks emerge in the power-voltage characteristics as a result of unequal solar irradiation, traditional MPPT techniquessuch as perturb and observe (P&O) and incremental conductance (IC) often struggle with accuracy and efficiency. Recent advancements have introduced metaheuristic optimization algorithms, such as the Dandelion Optimizer and Cuckoo Search, which have demonstrated superior performance in tracking the Global Maximum Power Point (GMPP) with high efficiency and rapid convergence. For instance, the Cuckoo Search algorithm combined with a Super Twisting Sliding Mode Controller has shown remarkable precision and resilience against uncertainties. Additionally, the Antlion Optimizer has outperformed traditional methods in both ideal and partial shading conditions, achieving faster response times and higher power outputs. These innovations highlight the transformative potential from traditional methods to Artificial Intelligence (AI) of FOARNN in optimizing solar energy systems, ensuring maximum energy extraction even in suboptimal conditions.