Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions

The number of decentralized photovoltaic (PV) systems generating electricity has increased significantly, and its monitoring and maintenance has become a challenge in terms of stability, reliability, security, efficiency, as well as energy production costs. Hence, prevention against faults and breakdowns becomes essential. In this work, a Convolutional Neural Network (CNN) model and a fine-tuned model based on Visual Geometry Group (VGG-16) have been examined to address the issue of fault diagnosis of PV modules using thermographic images. For fault detection, we have used binary classification, and multiclass classification for identification the type of fault. The database used in this study was made up of an imbalanced class distribution of infrared thermographic images of PV modules under normal and faulty conditions (such as bypass diode failure, partially covered PV module, shading effect, short-circuit and dust deposit on the PV surface). The test facility is located at the Unit for Developing Solar Equipment's (UDES), in the north of Algeria. The average accuracy archived using the fine-tuned VGG-16 model is 99.91% for the fault detection and 99.80% for the fault diagnosis of five types of defects. Experimental tests show high accurate prediction results using the fine-tuned model and somewhat less accuracy using the small Deep Convolutional Neural Network (small-DCNN) model.