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Supervised and Unsupervised Machine Learning of Electroluminescent Images of Photovoltaic Modules

Ahmad Maroof Karimi, Justin Fada, Roger French


Electroluminescence (EL) is a process in which materials emit light when an electric current is passed through it. In this method, electricity is passed through photovoltaic (PV) modules and EL light is emitted from the solar cells which are captured by an infrared sensitive camera. EL images are useful for characterization of electrical properties of photovoltaic (PV) modules based on the intensity of light in the images. The goal of the project is to build an automated pipeline for EL image supervised classification and unsupervised clustering. The motivation behind EL image processing is to study the effect of degradation in electrical properties based on physical appearances captured by the images. To study PV module degradation, EL images of crystalline silicon PV solar panels were captured under multiple test conditions at various periodic intervals. Damp-heat and thermal cycling cause corrosion and cracks, respectively, which can be seen in an EL image with regions of dark areas. Cracks orientation and thickness of corrosion is correlated to resistive losses which cause EL images to have lower light intensity at affected areas. This work is part of our US Dept. of Energy, SunShot project “MLEET”. To enable in-place analytics we store all datasets and results from different sources in Hadoop with an HBase NoSQL database and we integrate it with python using the happybase module. For feature extraction and machine learning from these EL images, we use scipy, sklearn, and opencv.