AI-Driven MPPT Optimization for Perovskite-Based Flexible Solar PV Panels in Partial Shading Conditions

Charles Ibeabuchi Mbonu¹ and John Saah Tamba II ²*

Department of Electrical and Electronic Engineering, Federal University of Technology Owerri, Imo State

Department of Electrical, Telecom. & Computer Engineering, Kampala International University, Uganda

*Corresponding Author: saah.tambaii@studwc.kiu.ac.ug

ABSTRACT

The integration of artificial intelligence (AI) into Maximum Power Point Tracking (MPPT) systems has emerged as a transformative solution for enhancing energy efficiency in perovskite-based flexible solar photovoltaic (PV) panels, particularly under partial shading conditions. This study explores the design, implementation, and evaluation of AI-driven MPPT techniques tailored for dynamic urban environments. Fabricated using advanced perovskite materials and encapsulated for flexibility and durability, these panels exhibit high power conversion efficiency and adaptability to non-traditional surfaces. Comparative analyses reveal that AI-based MPPT outperforms conventional methods in tracking accuracy, response time, and energy yield. The findings underscore the scalability and robustness of AI-driven systems, highlighting their potential for urban applications such as rooftop PV installations, solar-integrated windows, and portable solar devices. The study concludes that AI-enhanced MPPT systems significantly improve the viability of solar energy solutions in environments with non-uniform illumination, paving the way for sustainable urban energy infrastructures.

Keywords: Maximum Power Point Tracking, Solar PV, Artificial Intelligence, partial shading condition

CITE AS: Charles Ibeabuchi Mbonu and John Saah Tamba (2025). AI-Driven MPPT Optimization for Perovskite-Based Flexible Solar PV Panels in Partial Shading Conditions. IDOSR JOURNAL OF APPLIED SCIENCES 10(1):36-43. https://doi.org/10.59298/IDOSRJAS/2025/101.364300