Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection

Sharmarke Hassan*, Mahmoud Dhimish

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Detecting cracks in solar photovoltaic (PV) modules plays an important role in ensuring their performance and reliability. The development of convolutional neural networks (CNNs) has introduced a game-changing dimension in the detection of defects in PV modules. This paper proposes an automated defect detection method for PV, by leveraging custom-designed CNN to accurately analyse electroluminescence (EL) images, identifying defects such as cracks, mini-cracks, potential induced degradation (PID), and shaded areas. The proposed system achieves a high level of validation accuracy of 98.07%, reducing manual inspection demands, enhancing quality standards, and saving costs. The system was validated in a case study for PV installations faulty with PID, where it identified all defective modules with a high degree of precision of 96.6%, surpassing existing methods. This methodology holds promise for revolutionizing PV industry quality control, improving module reliability, and supporting sustainable solar energy growth.
Original languageEnglish
Article number119389
Number of pages13
JournalRenewable Energy
Volume219
Early online date9 Oct 2023
DOIs
Publication statusPublished - 1 Dec 2023

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

Keywords

  • Renewable energy
  • Solar Energy
  • Photovoltaic
  • Artificial intelligence
  • Machine learning
  • Convolutional neural network

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