Hybrid CNN-EML model for fault diagnosis in Electroluminescence images of photovoltaic cells

The quality inspection of solar module manufacturing is essential to guarantee photovoltaic (PV) power plants' steady. This paper presents the development of an innovative hybrid model that combines convolutional neural networks (CNN) with ensemble machine learning (EML) algorithms. The integration of these approaches was employed in order to develop a ranking weight voting system to the features extracted by the CNN model, by combining three fundamental algorithms: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). The advanced CNN-Ensemble Machine Learning (CNN-EML) technique is applied to a dataset of electroluminescence (EL) images featuring the nine most important and frequent defects. The results demonstrated that techniques based on the CNN-EML provide superior classification accuracy, effectively addressing the challenge of diagnosing faults in PV module manufacturing. The CNN-EML model achieved a significant accuracy of 94% in classification of different defects, outperforming CNN algorithm-based methods in the proposed comparative analysis.