Abstract Scope |
Solar modules used in field may suffer from degradation caused by defects, which can be detected with electroluminescence (EL) imaging. However, it is not feasible to analyze millions of EL images manually. Therefore, we develop an automatic pipeline to analyze EL images and detect various defect types including cracks, intra-cell defects, oxygen-induced defects and solder disconnection. We train neural networks including ResNet18, ResNet50, ResNet152 and YOLO models with 896 EL images of solar modules, and determine that ResNet18 and YOLO are the best-performing models with macro f1 scores of 0.83 for ResNet18 and 0.78 for YOLO on the testing set(129 images of modules). We provide a detailed analysis of the selection of the models based on users’ demands. Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze over 18,000 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. |