Predicting Module I-V Curves from Electroluminescence Images with Deep Learning

Abstract

Electroluminescence images have been used to qualify the performance of PV modules. Yet, to assess the status and techno-economic performance of a module in a string, quantitative information is required. In this work, we propose a method to predict PV module IV curves from electroluminescence images using a deep learning algorithm. The proposed method consists of creating eleven deep learning models that predict ten points on the IV curve, including ISC, Impp, Vmpp and VOC. We test this method on a dataset of 574 electroluminescence images and IV curves with one dominant fault: inactive cell areas. Results show that the deep learning models are able to find a relationship between the inactive areas of the PV module electroluminescence image and the PV module IV curve. For the test dataset, we predict IV curves with good accuracy values and a mean absolute error for module power below 5 W