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PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility

Author

Listed:
  • Muhammad Hussain

    (Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK)

  • Hussain Al-Aqrabi

    (Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
    Faculty of Computer Information System (CIS), Higher Colleges of Technology, University City, Sharjah P.O. Box 7947, United Arab Emirates)

  • Richard Hill

    (Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK)

Abstract

Photovoltaic cell manufacturing is a rigorous process involving many stages where the cell surface is exposed to external pressure and temperature differentials. This provides fertile ground for micro-cracks to develop on the cell surface. At present, domain experts carry out a manual inspection of the cell surface to judge if any micro-cracks are present. This research looks to overcome the issue of cell data scarcity through the proposed filter-induced augmentations, thus providing developers with an effective, cost-free mechanism for generating representative data samples. Due to the abstract nature of the cell surfaces, the proposed augmentation strategy is effective in generating representative samples for better generalization. Furthermore, a custom architecture is developed that is computationally lightweight compared to state-of-the-art architectures, containing only 7.01 million learnable parameters while achieving an F1-score of 97%.

Suggested Citation

  • Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility," Energies, MDPI, vol. 15(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8667-:d:977087
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    References listed on IDEAS

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    1. Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection," Energies, MDPI, vol. 15(15), pages 1-14, July.
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    Cited by:

    1. Tahir Hussain & Muhammad Hussain & Hussain Al-Aqrabi & Tariq Alsboui & Richard Hill, 2023. "A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision," Energies, MDPI, vol. 16(10), pages 1-19, May.

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