Case Study: Utilising of Deep Learning Models for Fault Detection and Diagnosis of Photovoltaic Modules to Improve Solar Energy Constructions’ O&M Activities Quality

Renewable energy sources have long been considered to be the sole alternatives to fossil fuels. Consequently, the usage of 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, which is the systems’ most crucial component. Currently, fault detection and diagnosis are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning have proven its feasibility in image classification and object detection. Thus, deep learning can be extended to visual fault detection using data generated by electroluminescence imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of electroluminescence data, as well as several techniques based on both supervised and unsupervised learning to detect and diagnose visual faults and defects presented in a module.