Embedded Hybrid Model (CNN-ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images
Benghanem, Mohamed ， Mellit, Adel ， Moussaoui, Chourouk
2023-05-10 SUSTAINABILITY 2023 15(卷), 10(期), (null页)
JCR分区:
影响因子:
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.
相关推荐
- 4E assessment and production optimization of conical solar distillers: Investigating the dual effect of magnetic field distribution of disc magnets with external truncated reflectors [2023-05-10]
- Performance improvement and optimization of hemispherical solar stills using different buoyancy levels of plastic pipes in water basins: surface tension optimization [2023-05-10]
- A Novel Embedded System for Real-Time Fault Diagnosis of Photovoltaic Modules [2023-05-10]
- Evaluation of the Performance of Polycrystalline and Monocrystalline PV Technologies in a Hot and Arid Region: An Experimental Analysis [2023-05-10]
- IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse Farm in an Arid Region [2023-05-10]