Fault detection from PV images using hybrid deep learning model

Monitoring and maintenance of photovoltaic (PV) systems are critical in order to ensure continuous power generation and prevent operation drops. Manual inspection of high-resolution Electroluminescence (EL) images of PV modules requires human effort and time. Some research rely on manually created features, which cannot ensure the classification stage's effectiveness. Contrarily, Deep learning models have been widely used for fast and accurate image classification. However, these standard deep learning models could produce errors, especially in the presence of noisy or inter-class small variation data which is the case with PV images. In this paper, we introduce an end-to-end deep learning model that combines handcrafted and automatic feature extraction to produce better PV image classification accuracy. Using a deep neural network and histogram of oriented gradient (HoG) of PV images, this work makes a significant contribution by directly learning a hybrid model that refines the leveraged feature vector. Our experimental results show better performance compared with six state-of-the-art methods that use the same or different baseline deep learning model.