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Method for the Automated Inspection of the Surfaces of Photovoltaic Modules

Author

Listed:
  • Pavel Kuznetsov

    (Institute of Nuclear Energy and Industry, Sevastopol State University, 299053 Sevastopol, Russia)

  • Dmitry Kotelnikov

    (Institute of Nuclear Energy and Industry, Sevastopol State University, 299053 Sevastopol, Russia)

  • Leonid Yuferev

    (Institute of Nuclear Energy and Industry, Sevastopol State University, 299053 Sevastopol, Russia)

  • Vladimir Panchenko

    (Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia)

  • Vadim Bolshev

    (Laboratory of Power and Heat Supply, Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia)

  • Marek Jasiński

    (WWSIS “Horyzont”, 54-239 Wrocław, Poland)

  • Aymen Flah

    (National Engineering School of Gabès, Processes, Energy, Environment and Electrical Systems, University of Gabès, LR18ES34, Gabes 6072, Tunisia)

Abstract

One of the most important conditions for the efficient operation of solar power plants with a large installed capacity is to ensure the systematic monitoring of the surface condition of the photovoltaic modules. This procedure is aimed at the timely detection of external damage to the modules, as well as their partial shading. The implementation of these measures solely through visual inspection by the maintenance personnel of the power plant requires significant labor intensity due to the large areas of the generation fields and the operating conditions. Authors propose an approach aimed at increasing the energy efficiency of high-power solar power plants by automating the inspection procedures of the surfaces of photovoltaic modules. The solution is based on the use of an unmanned aerial vehicle with a payload capable of video and geospatial data recording. To perform the procedures for detecting problem modules, it is proposed to use “object-detection” technology, which uses neural network classification methods characterized by high adaptability to various image parameters. The results of testing the technology showed that the use of a neural network based on the R-CNN architecture with the learning algorithm—Inception v2 (COCO)—allows detecting problematic photovoltaic modules with an accuracy of more than 95% on a clear day.

Suggested Citation

  • Pavel Kuznetsov & Dmitry Kotelnikov & Leonid Yuferev & Vladimir Panchenko & Vadim Bolshev & Marek Jasiński & Aymen Flah, 2022. "Method for the Automated Inspection of the Surfaces of Photovoltaic Modules," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11930-:d:921420
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    References listed on IDEAS

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    1. Václav Beránek & Tomáš Olšan & Martin Libra & Vladislav Poulek & Jan Sedláček & Minh-Quan Dang & Igor I. Tyukhov, 2018. "New Monitoring System for Photovoltaic Power Plants’ Management," Energies, MDPI, vol. 11(10), pages 1-13, September.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    3. Idris Al Siyabi & Arwa Al Mayasi & Aiman Al Shukaili & Sourav Khanna, 2021. "Effect of Soiling on Solar Photovoltaic Performance under Desert Climatic Conditions," Energies, MDPI, vol. 14(3), pages 1-18, January.
    4. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    5. Martin Libra & Milan Daneček & Jan Lešetický & Vladislav Poulek & Jan Sedláček & Václav Beránek, 2019. "Monitoring of Defects of a Photovoltaic Power Plant Using a Drone," Energies, MDPI, vol. 12(5), pages 1-9, February.
    6. Hafiz Suliman Munawar & Fahim Ullah & Siddra Qayyum & Sara Imran Khan & Mohammad Mojtahedi, 2021. "UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
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