Variational Autoencoder for EL Image Analysis for Cell Crack Power Loss Prediction

作者：Norman Jost, Brandon Byford, Benjamin G. Pierce, Emma Cooper, Ojas Sanghi, Jennifer L. Braid · 年份：2025 · DOI：10.1109/pvsc59419.2025.11132908 · 研究领域：Industrial Vision Systems and Defect Detection、Infrastructure Maintenance and Monitoring、Vehicle License Plate Recognition

Due to the extreme thermomechanical stresses imposed on photovoltaic (PV) modules during manufacture, shipping, installation, regular operation, and extreme weather, fracture of Si cells may be inevitable. As the industry moves thinner glass and larger format modules, and as the frequency of extreme weather events continues to increase, the problem of cell cracks is likely here to stay. Therefore, when cracks are observed, it is important to understand their immediate effects on PV performance. In this work we are using a Variational Autoencoder (VAE) trained with preprocessed EL images. The results of the training, latent space, is analyzed to detect clusters of certain defects. IV-curves of the same solar cells used for the EL images are used to develop a correlation model between the latent space and the performance loss.