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Using CNNs for Photovoltaic Panel Defect Detection via Infrared Thermography to Support Industry 4.0

Author

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  • Spajić Mislav

    (Algebra University, Croatia)

  • Talajić Mirko

    (Algebra University, Croatia)

  • Mršić Leo

    (Algebra University, Croatia)

Abstract

Background This study demonstrates how convolutional neural networks (CNNs), supported by open-source software and guided by corporate social responsibility (CSR), can enhance photovoltaic (PV) panel maintenance. Connecting industrial informatics with sustainable practices underscores the potential for more efficient and responsible energy systems within Industry 4.0. The rapid expansion of solar power necessitates effective maintenance and inspection of PV panels to ensure optimal performance and longevity. CNNs have emerged as potent tools for detecting defects in PV panels through infrared thermography (IRT). Objectives The review aims to evaluate CNNs’ effectiveness in detecting PV panel defects, align their capabilities with the IEC TS 62446-3:2017 standard, and assess their economic benefits. Methods/Approach A systematic review of literature focused on studies using CNNs and IRT for PV panel defect detection. The analysis compared performance metrics, economic benefits, and alignment with industry standards. Results CNN models demonstrated high accuracy in defect detection, with most achieving above 90%. Integrating UAVs for image acquisition significantly reduced inspection times and costs. Conclusions CNNs are highly effective in detecting PV panel defects, offering substantial economic benefits and potential for industry-wide standardisation. Further research is needed to enhance model robustness across diverse conditions and PV technologies.

Suggested Citation

  • Spajić Mislav & Talajić Mirko & Mršić Leo, 2024. "Using CNNs for Photovoltaic Panel Defect Detection via Infrared Thermography to Support Industry 4.0," Business Systems Research, Sciendo, vol. 15(1), pages 45-66.
  • Handle: RePEc:bit:bsrysr:v:15:y:2024:i:1:p:45-66:n:1003
    DOI: 10.2478/bsrj-2024-0003
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    More about this item

    Keywords

    Convolutional Neural Networks; Photovoltaic Panels; Defect Detection; Infrared Thermography; Solar Energy;
    All these keywords.

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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