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Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

Author

Listed:
  • Giovanni Cipriani

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

  • Antonino D’Amico

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

  • Stefania Guarino

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

  • Donatella Manno

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

  • Marzia Traverso

    (Institute of Sustainability in Civil Engineering (INaB), RWTH Aachen University, D-52074 Aachen, Germany)

  • Vincenzo Di Dio

    (Department of Engineering, University of Palermo, 90133 Palermo, Italy)

Abstract

This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses.

Suggested Citation

  • Giovanni Cipriani & Antonino D’Amico & Stefania Guarino & Donatella Manno & Marzia Traverso & Vincenzo Di Dio, 2020. "Convolutional Neural Network for Dust and Hotspot Classification in PV Modules," Energies, MDPI, vol. 13(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6357-:d:454796
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    References listed on IDEAS

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    Cited by:

    1. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
    2. Gianfranco Di Lorenzo & Erika Stracqualursi & Rodolfo Araneo, 2022. "The Journey Towards the Energy Transition: Perspectives from the International Conference on Environment and Electrical Engineering (EEEIC)," Energies, MDPI, vol. 15(18), pages 1-5, September.
    3. 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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