Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
Date:
In this talk, I highlighted the growing need for reliable fault detection and diagnosis (FDD) in photovoltaic (PV) manufacturing as global solar adoption accelerates. Traditional FDD relies on specialized instruments and expert analysis, creating bottlenecks in quality assurance. I introduced a deep learning–based approach that leverages electroluminescence (EL) imaging to automatically identify visual defects in PV modules. The presentation covered exploratory data analysis of EL images and several supervised learning techniques designed to detect and classify module faults, demonstrating how modern DL methods can significantly enhance PV quality control and support scalable, high-precision manufacturing.