Automatic Classification of Defective Photovoltaic Module Cells Based on a Novel CNN-PCA-SVM Deep Hybrid Model in Electroluminescence Images

In today’s world, the rapid development of photovoltaic (PV) power plants has facilitated sustainable energy production. Maintenance and defect detection play crucial roles in ensuring the continuity of energy production. The manual inspection of electroluminescence (EL) images of PV modules requires significant human power and time investment. This study presents a method for the automatic fault detection of PV cells in EL images using hybrid deep features optimized with a principal component analysis (PCA) feature selection algorithm. A lightweight and high-performance model that combines the strengths of convolutional neural network (CNN) architectures was proposed. First, data augmentation techniques were employed owing to the imbalance between the defective and functional classes in the dataset containing EL images. In experimental studies conducted by integrating the PCA algorithm into MobileNetV2, DenseNet201, and InceptionV3 CNN models, accuracy, precision, recall, and F1-score values of 92.19%, 92%, 90%, and 91%, respectively, were achieved. When the results were analyzed, it was observed that the proposed method was effective in detecting faults in PV panel cells.