Publications

The growing global demand for clean, sustainable energy has driven extensive research into renewable energytechnologies, with solar energy emerging as a highly promising solution. Solar photovoltaic (PV) systems areincreasingly adopted for their ability to convert sunlight into electricity, providing an environmentally friendlyalternative to fossil fuels. However, the performance of PV systems is significantly influenced by environmentalfactors, particularly solar irradiance and temperature, which lead to fluctuations in power output. This studyexplores the application of Artificial Intelligence (AI)-based Maximum Power Point Tracking (MPPT) techniquesto 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 optimizationenhances the efficiency, stability, and scalability of solar energy systems, particularly in rural electrification andindustrial energy management. Among traditional MPPT methods, the Optimized Adaptive DifferentialConductance (OADC) technique is notable for its simplicity, cost-effectiveness, and ease of implementation, whilethe Scanning Particle Swarm Optimization (SPSO) technique stands out for its superior tracking accuracy and abilityto achieve real-time convergence to the Maximum Power Point