Norazlan Hashim received the B.Eng. and M.Eng. degrees in Electrical Engineering from Universiti Malaya, Kuala Lumpur, Malaysia, in 2001 and 2007, respectively, and the Ph.D. degree in Electrical Engineering from Universiti Teknologi Malaysia, Johor, in 2022. He is currently a Senior Lecturer at the School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM), Malaysia. His research interests include Maximum Power Point Tracking, Power Electronic Converters, Artificial Intelligence, Photovoltaic Systems, and Educational Technology

Abstract Title : AI-Driven Intelligent Architecture for Maximum Power Point Tracking in Photovoltaic Energy Systems AbstractThe increasing integration of photovoltaic energy into modern power systems requires intelligent, reliable, and computationally efficient control architectures to maximise energy extraction under rapidly changing environmental conditions. Conventional maximum power point tracking methods often experience performance degradation during partial shading because multiple local maximum power points may appear on the photovoltaic power-voltage characteristic. This condition increases the risk of inaccurate tracking, premature convergence, and transient power losses. This presentation discusses an artificial intelligence-driven architecture for maximum power point tracking in photovoltaic systems. The proposed approach integrates metaheuristic optimisation, adaptive search mechanisms, real-time data acquisition, and intelligent decision-making to identify the global maximum power point under uniform and partial shading conditions. Particular attention is given to the balance between exploration and exploitation, convergence speed, tracking reliability, computational burden, and suitability for implementation on low-cost embedded hardware. The findings demonstrate how intelligent optimisation techniques can improve the efficiency, adaptability, and reliability of photovoltaic energy systems. The work provides a foundation for developing autonomous renewable-energy solutions that combine AI-based control with practical embedded implementation