Artificial Intelligence-Based Maximum Power Point Tracking for Standalone Solar Photovoltaic Systems: Enhancing Efficiency and Adaptability

The growing global demand for clean and sustainable energy has driven advancements in solar photovoltaic (PV) systems, which offer an environmentally friendly and renewable alternative to conventional fossil fuels. However, the efficiency of PV systems is significantly affected by variations in solar irradiance and temperature, necessitating the use of Maximum Power Point Tracking (MPPT) algorithms. Traditional MPPT techniques, such as Perturb and Observe (P&O) and Incremental Conductance (IC), suffer from slow response times, steady-state oscillations, and challenges in handling partial shading conditions. To address these limitations, this study explores the integration of Artificial Intelligence (AI)-based MPPT algorithms, including Artificial Neural Networks (ANNs) and Fuzzy Logic Controllers (FLCs), to enhance tracking precision, minimize power losses, and improve system adaptability. Experimental and simulation results demonstrate that AI-driven MPPT techniques outperform conventional methods in dynamic environmental conditions, leading to higher energy conversion efficiency and improved reliability of standalone solar PV systems. The findings underscore the potential of AI-based MPPT as a transformative approach to optimizing renewable energy utilization. Keywords: Renewable energy, Standalone solar PV, Fabrication, MPPT, Solar PV efficiency