Photovoltaic Module Defects Classification Analysis Using ShuffleNet Architecture in Electroluminescence Images

The capacity of solar energy worldwide has grown significantly, from 40.277 to 580.159 MW over the last 9 years. The operation of solar panels is prone to defects due to changes in weather or the environment. Different types of defects can occur depending on the phase of the module, such as infant, midlife, or wear-out failure. Several types of defect images can be used to identify photovoltaic panel (PV) defects such as RGB, thermography, and Electroluminescence (EL) images. Recently, many researchers used EL images for PV defect classification due to their availability to create high-contrast patterns on PV modules, making it easier to detect defects, such as cracks or broken cells, that might be difficult to spot with the naked eye. Therefore, this work aims to analyze and classify PV defects in EL images using ShuffleNet, a Convolution Neural Network (CNN) based architecture. For comparison purposes, other CNN architectures, namely MobileNet and SqueezeNet will be implemented. The results show that the ShuffleNet architecture outperforms MobileNet and SqueezeNet architectures in terms of precision (92.53%), recall (92.24%), and F1-score (93.17%) in classifying EL PV module defect images.