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Original Article

AI-Enhanced IoT-Based Solar Panel Fault Detection Device Using Edge AI, Machine Learning, and Digital Twin Technology for Real-Time Photovoltaic System Monitoring

Open Access Peer Reviewed Crossref DOI

Authors

  • Thendral S1
  • Tarika S2
  • Niranjana A3
  • Naresh kumar R4

1 2 3 4 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India.

Article Information

Published Online
July-August 2026
Pages
72-79
Article Type
Original Article
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References

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classification,”. Sensors, vol. 24, no. 22, pp. 1–15, 2024.
2. M. Islam, M. R. Rashel, M. T. Ahmed, and A. K. M. K. Islam, “Artificial intelligence in photovoltaic fault identification and diagnosis: A
systematic review,”. Energies, vol. 16, no. 21, pp. 1–30, 2023.
3. S. R. Joshua, K. Y. Palilingan, S. P. Lengkong, and S. Park, “Deep learning-driven solar fault detection in solar–hydrogen AIoT systems,”
. Hydrogen, vol. 7, no. 1, pp. 1–12, 2025.
4. S. M. Patil and K. S. Kadam, “Automated smart solar panel system fault detection using CNN and deep learning,” . Journal of Neonatal
Surgery, vol. 14, no. 2, pp. 1–10, 2025.
5. “Advancements in AI-driven detection and localisation of solar panel defects,”. Advanced Engineering Informatics, vol. 64, pp. 102–110,
2025.
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Image, Graphics and Signal Processing, vol. 17, no. 4, pp. 1–10, 2025.
7. “Convolutional neural networks and Internet of Things for fault detection in photovoltaic plants,”. Measurement, vol. 234, pp. 1–12, 2024.
8. R. Kavitha, S. Dhanush, S. M. Ismail, and D. Shyam, “Solar panel fault detection using artificial intelligence,” in Proc. Int ernational
Conference on Advances in Electronics, Computers and Communications, 2025, pp. 1–6.
9. A. Mellit and S. A. Kalogirou,. “Artificial intelligence techniques for photovoltaic applications: A review,” Progress in Energy and
Combustion Science, vol. 34, no. 5, pp. 574–632, 2008.
10. M. Dhimish, V. Holmes, B. Mehrdadi, and M. Dales, . “Simultaneous fault detection algorithm for grid-connected photovoltaic plants,”.
IET Renewable Power Generation, vol. 11, no. 12, pp. 1565–1575, 2017.
11. A.Chouder and S. Silvestre, .“Automatic supervision and fault detection of PV systems based on power vol. 51, no. 10, pp. 1929–
1937, 2010.
12. Chine, A. Mellit, A. M. Pavan, and A. Lughi,. “Fault detection method for grid-connected photovoltaic plants using supervised learning,”.
Solar Energy, vol. 182, pp. 268–278, 2019.
13. S. Silvestre, A. Chouder, and E. Karatepe, “Automatic fault detection in grid-connected PV systems,”. Solar Energy, vol. 94, pp. 119–127,
2013.
14. A. Y. Jaen-Cuellar, L. Morales-Mendoza, and R. de J. Romero-Troncoso,. “Fault detection in photovoltaic systems using machine learning
techniques,”. IEEE Access, vol. 8, pp. 151–160, 2020.
15. M. A. Eltawil and Z. Zhao, .“Grid-connected photovoltaic power systems: Technical and potential problems—A review,”. Renewable and
Sustainable Energy Reviews, vol. 14, no. 1, pp. 112–129,2010.
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