Detection of Defective Solar Panel Cells in Electroluminescence Images with Deep Learning

Abdil Karakan ()
Additional contact information
Abdil Karakan: Dazkırı Vocational School, Afyon Kocatepe University, 03950 Afyonkarahisar, Türkiye

Sustainability, 2025, vol. 17, issue 3, 1-17

Abstract: In this study, faults in solar panel cells were detected and classified very quickly and accurately using deep learning and electroluminescence images together. A unique and new dataset was created for this study. Monocrystalline and polycrystalline solar panel cells were used in the dataset. The dataset included intact, cracked and broken images for each solar panel cell. The dataset was preprocessed and multiplied to equalize the intact, cracked and broken numbers. Seven different deep learning architectures were used in this study. As a result of this study, 97.82% accuracy was achieved for the monocrystalline solar panel cells and 96.29% for the polycrystalline solar panel cells in the SqueezeNet architecture.

Keywords: deep learning; solar panel cells; electroluminescence; fault detection and diagnosis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/3/1141/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/3/1141/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:3:p:1141-:d:1580626

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().