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

Author

Listed:
  • Mohamed Benghanem

    (Department of Physics, Faculty of Science, Islamic University of Madinah, Madinah 42351, Saudi Arabia)

  • Adel Mellit

    (Department of Electronics, Faculty of Sciences and Technology, University of Jijel, Jijel 18000, Algeria)

  • Chourouk Moussaoui

    (Department of Electronics, Faculty of Sciences and Technology, University of Jijel, Jijel 18000, Algeria)

Abstract

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.

Suggested Citation

  • Mohamed Benghanem & Adel Mellit & Chourouk Moussaoui, 2023. "Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7811-:d:1143588
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    References listed on IDEAS

    as
    1. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    2. Meng-Hui Wang & Zong-Han Lin & Shiue-Der Lu, 2022. "A Fault Detection Method Based on CNN and Symmetrized Dot Pattern for PV Modules," Energies, MDPI, vol. 15(17), pages 1-17, September.
    3. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Roberto Pierdicca & Marina Paolanti & Andrea Felicetti & Fabio Piccinini & Primo Zingaretti, 2020. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images," Energies, MDPI, vol. 13(24), pages 1-17, December.
    5. Fonseca Alves, Ricardo Henrique & Deus Júnior, Getúlio Antero de & Marra, Enes Gonçalves & Lemos, Rodrigo Pinto, 2021. "Automatic fault classification in photovoltaic modules using Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 179(C), pages 502-516.
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    1. Fuxun Chen & Lanxin Zhang & Siyu Kang & Lutong Chen & Honghong Dong & Dan Li & Xiaozhu Wu, 2023. "Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images," Sustainability, MDPI, vol. 15(14), pages 1-18, July.

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