Detección de fallas en paneles solares fotovoltaicos utilizando procesamiento de imágenes SWIR y técnica de electroluminiscencia

Abstract

The growth of the solar industry has driven the need for effective methods to detect defects in photovoltaic systems, which inspired this study focused on the application of image processing techniques in the short-wave infrared spectrum (SWIR) through electroluminescence. Thiswork centers on the design and implementation of an experimental methodology to identify defects in commercial photovoltaic panels, using an OWL 640 M camera to capture images and combining image processing techniques with machine learning to detect cracks, interrupted contacts, and material anomalies. The proposed approach includes stages such as equipment calibration, individual cell analysis, and system performance evaluation, employing convolutional neural networks and a dataset tailored to the characteristics of the images. The results not only contribute to improving the efficiency and reliability of photovoltaic systems but also present opportunities in fields such as electronic component detection, humidity identification, liquid measurement, and contaminant analysis in food, highlighting the multidisciplinary impact of SWIR imaging.