Embedded Hybrid Model (CNN-ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images

In this paper, a novel hybrid model for the fault diagnosis of photovoltaic (PV) modules was developed. The model combines a convolutional neural network (CNN) with a machine learning (ML) algorithm. A total of seven defects were considered in this study: sand accumulated on PV modules, covered PV modules, cracked PV modules, degradation, dirty PV modules, short-circuited PV modules, and overheated bypass diodes. First, the hybrid CNN-ML has been developed to classify the seven common defects that occur in PV modules. Second, the developed model has been then optimized. Third, the optimized model has been implemented into a microprocessor (Raspberry Pi 4) for real-time application. Finally, a friendly graphical user interface (GUI) has been designed to help users analyze their PV modules. The proposed hybrid model was extensively evaluated by a comprehensive database collected from three regions with different climatic conditions (Mediterranean, arid, and semi-arid climates). Experimental tests showed the feasibility of such an embedded solution in the diagnosis of PV modules. A comparative study with the state-of-the-art models and our model has been also presented in this paper.