AI-Driven MOSFET Bypass Systems and Computer Vision for Mitigating Partial Shading Losses in Photovoltaic Systems

This study investigates the potential of AI-driven MOSFET bypass systems and computer vision to mitigate partial shading losses in photovoltaic (PV) systems. Partial shading, particularly due to cloud cover, can significantly reduce the power output of PV systems. As commonly used bypass diodes have limitations due to power loss and hotspots, this study explores the use of MOSFETs as an alternative, paired with a convolutional neural network (CNN) based computer vision-based cloud detection system. The CNN model is trained to detect cloud edges and track their movement, enabling dynamic adjustment of the MOSFET bypass system. The results show that the MOSFET bypass system outperforms traditional bypass diodes in energy efficiency, particularly in medium to low intensity static shading conditions. The study also highlights the potential of predictive analysis to improve the accuracy of detection and MOSFET control for dynamic shading conditions. Findings suggest that AI-driven MOSFET bypass systems and computer vision can be an effective solution for mitigating partial shading losses in PV systems, particularly in large-scale solar arrays. Future studies can focus on refining the CNN model, exploring alternative control methods, and further investigating the energy efficiency of the system under various shading conditions.