Defect Detection in Photovoltaic Module Cell Using CNN Model

One way of examining surface defects on photovoltaic modules is the Electroluminescence (EL) imaging technique. The data set used in this work is an open data set for fault detection and classification of photovoltaic cells. In this article, we have used various deep learning (DL) techniques to ensure fault detection and diagnosis of photovoltaic modules. A binary classification model was developed that highlighted defective PV modules and normal modules. The subset of defective PV modules was used to design a multi-class model of default detection (light, moderate, and severe). Evaluation results (confusion matrix, mean square error (mse)) showed that methods based on deep learning performed exceptionally well, making it possible to solve the problem of detecting and diagnosing faults in photovoltaic modules with good overall precision (mse = 0.060).