Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture

Abstract

The usage of photovoltaic (PV) systems has experienced exponential growth. This growth, however, places gargantuan pressure on the solar energy industry’s manufacturing sector and subsequently begets issues associated with the quality of PV systems, especially the PV module. Currently, fault detection and diagnosis (FDD) are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning (DL) have proven its feasibility in image classification and object detection. Thus, DL can be extended to visual fault detection using data generated by electroluminescence (EL) imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of EL data and several techniques based on supervised learning to detect and diagnose visual faults and defects presented in a module.