Abstract

The growing global demand for clean, sustainable energy has driven extensive research into renewable energy
technologies, with solar energy emerging as a highly promising solution. Solar photovoltaic (PV) systems are
increasingly adopted for their ability to convert sunlight into electricity, providing an environmentally friendly
alternative to fossil fuels. However, the performance of PV systems is significantly influenced by environmental
factors, particularly solar irradiance and temperature, which lead to fluctuations in power output. This study
explores the application of Artificial Intelligence (AI)-based Maximum Power Point Tracking (MPPT) techniques
to optimize the efficiency of PV systems. AI-driven MPPT controllers, incorporating machine learning, fuzzy logic,
and genetic algorithms, offer enhanced adaptability, responsiveness, and efficiency compared to traditional methods.
The research focuses on the design, development, and evaluation of an AI-optimized MPPT controller prototype,
demonstrating the potential of AI to overcome the limitations of conventional MPPT techniques. This optimization
enhances the efficiency, stability, and scalability of solar energy systems, particularly in rural electrification and
industrial energy management. Among traditional MPPT methods, the Optimized Adaptive Differential
Conductance (OADC) technique is notable for its simplicity, cost-effectiveness, and ease of implementation, while
the Scanning Particle Swarm Optimization (SPSO) technique stands out for its superior tracking accuracy and ability
to achieve real-time convergence to the Maximum Power Point.