Performance of fault classification on Photovoltaic modules using Thermographic images

Abstract

In this paper, presents thermal image analysis on Fault Classification (FDC) of Photovoltaic (PV) Module. The traditional manual approach of PV inspection is generally more time-consuming, more dangerous, and less accurate than the modern approach of PV inspection using Thermography Images (TI). The benefit in using (TI) images is that it can be used to quickly establish problematic areas in PV Module and provide various measurement details. Thermal image analysis conducted in this research will contribute to inspect PV module by providing a more accurate and cost-efficient diagnosis of PV faults. To maintain the long-term reliability of solar modules and maximize the power output, faults in modules need to be diagnosed at an early stage. In this research, thermographic images were used to detect faults in PV Module using traditional methods and Deep learning methods are mainly used to identify and classify the type of faults that can happen in PV Module. This method will present and discuss on the fault classification and its performance parameters. The fault detection stage determined whether the PV module has an abnormal condition. In this research, performance metrics of fault classification using Deep Neural Networks (DNNs) models is analyzed, which offers high accuracy for detecting abnormalities in image classification tasks.