Optimization of SWIR Image Capture and Processing for Defect Detection in Photovoltaic Panels

Authors

  • Franklin Gómez-López Facultad de Ingeniería, Universidad de Cuenca, Cuenca, Ecuador https://orcid.org/0009-0006-0355-9389
  • Danny Ochoa-Correa Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones, Universidad de Cuenca, Cuenca, Ecuador https://orcid.org/0000-0001-5633-1480
  • Isabel Cabrera-Carrera Facultad de Ingeniería, Universidad de Cuenca, Cuenca, Ecuador

DOI:

https://doi.org/10.54753/cedamaz.v15i1.2487

Keywords:

Electroluminescence, SWIR, Photovoltaic Panels, Image Processing, Preventive Maintenance

Abstract

This work introduces a methodology for capturing and processing Short-Wave Infrared (SWIR) images focused on detecting structural defects in photovoltaic panels. Indium Gallium Arsenide (InGaAs) sensors were used in combination with perspective correction, background subtraction, and contrast enhancement through the CLAHE algorithm. Experimental testing showed that adjusting capture parameters appropriately, along with efficient preprocessing, allows precise identification of defects such as cracks, inactive zones, and discontinuities in collector bars. This approach supports preventive maintenance strategies and helps extend the operational lifespan of photovoltaic installations.

References

Buerhop-Lutz, C., Koehl, M., and Frick, T. (2018). Electroluminescence imaging for quality control of photovoltaic modules. Progress in Photovoltaics: Research and Applications, 26(9), 719–727.

Chen, L., He, J., and Tan, X. (2021). Defect detection in electroluminescence images of PV modules using deep learning and attention mechanisms. IEEE Access, 9, 160795–160806.

Espinoza, J. L., González, L. G., and Sempértegui, R. (2017). Micro grid laboratory as a tool for research on non-conventional energy sources in Ecuador. In 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 1–7.

International Electrotechnical Commission. (2021). IEC TS 60904-13: Photovoltaic Devices – Part 13: Electroluminescence Imaging for Crystalline Silicon Photovoltaic Modules. Geneva, Switzerland.

Jordan, D. C., and Kurtz, S. R. (2017). Photovoltaic degradation rates—an analytical review. Progress in Photovoltaics: Research and Applications, 25(8), 668–691.

Li, J., Zhang, P., and Wang, X. (2022). Detection of PV module defects using short-wave infrared electroluminescence imaging and advanced segmentation techniques. Solar Energy, 235, 285–294.

Lofstad-Lie, V., Aarseth, B. L., Roosloot, N., Marstein, E. S., and Skauli, T. (2024). Modeling cost-effectiveness of photovoltaic module replacement based on quantitative assessment of defect power loss. Solar, 4(4), 728–743.

Matusz-Kalász, D., Bodnár, I., and Jobbágy, M. (2025). An overview of CNN-based image analysis in solar cells, photovoltaic modules, and power plants. Applied Sciences, 15(10), 5511.

Mei, Q., Han, Y., and Zhao, M. (2020). A review of short-wave infrared (SWIR) imaging in industrial applications. Infrared Physics and Technology, 104, 103134.

Qin, Y., Wang, X., and Zhao, L. (2021). Application of deep learning techniques for PV panel defect detection using electroluminescence images. IEEE Transactions on Industrial Informatics, 17(9), 6485–6494.

Redondo-Plaza, A., Velasco-Bonilla, A. Z., Morales-Aragones, J. I., Zorita-Lamadrid, L., Alonso-Gómez, V., and Hernández-Callejo, L. (2025). Electroluminescence imaging based on FFT analysis for outdoor photovoltaic module inspection: A self-powered signal modulation approach. Applied Sciences, 15(9), 4606.

Rehman, A., Khan, U., and Zafar, A. (2023). Enhancement of PV module inspection using CLAHE-based image processing. Journal of Imaging, 9(2), 45.

Zhang, W., Liu, H., and Chen, Z. (2022). A comprehensive review on defect detection methods in photovoltaic panels. Renewable and Sustainable Energy Reviews, 153, 111730.

Published

2025-06-30

How to Cite

Gómez-López, F., Ochoa-Correa, D., & Cabrera-Carrera, I. (2025). Optimization of SWIR Image Capture and Processing for Defect Detection in Photovoltaic Panels. CEDAMAZ, 15(1), 48–54. https://doi.org/10.54753/cedamaz.v15i1.2487

Issue

Section

Ciencias exactas e ingenierías

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