Sistema de clasificación de fallas en tiempo real de paneles fotovoltaicos mediante uso de imágenes RGB y aprendizaje profundo (2020)
This study presents a proposal for a classification system for faults that occur in photovoltaic modules from the use of RGB images and a deep learning approach implemented in a portable embedded device (Raspberry Pi). Convolutional Neural Networks (CNN) are used for photovoltaic module detection using semantic segmentation and additionally for fault classification. This study considers 2 specific cases of classification; Binary (Fault and not Fault) and quaternary (Cracks, Shadows, Dust and Not Fault). The models presented show an average accuracy greater than 90% for the binary classification and 74% for the quaternary classification. The implementation of these models in low cost embedded devices corresponds to a viable alternative for the continuous monitoring of photovoltaic modules.