Early Hotspot Detection in Photovoltaic Modules using Deep Learning Methods

Abstract

Photovoltaic (PV) energy systems have been widely used in energy production especially in recent years due to their clean, reliable, environmentally friendly and resource continuity. The electrical energy to be obtained must be uninterrupted and of high quality. For this reason, faults that may cause production losses in PV power plants should be detected quickly and accurately. In this study, an approach that can automatically detect hotspot fault in solar PV modules is presented. In the proposed approach, firstly, Jet colormap based colored image transformation process is applied to gray scale infrared image data. Then, using three different deep learning models such as MobilNetV2, ResNet-18, InceptionV3, both original grey scale data and Jet colormap data are detected and evaluation metric values are obtained. The importance of this research lies in the potential of deep learning-based effective detection, especially in the early and rapid diagnosis of PV hotspot faults.