Machine Learning-Driven Thermal Imaging for Fault Detection in Solar PV Systems

Authors

  • G. Tejaswi, Gudugunta Kalyani, Chilukuri Damini, Gangapatnam Varshitha

Keywords:

Solar Photovoltaic (PV) Systems, Thermal Imaging, Fault Detection, Renewable Energy, Smart Energy Monitoring, India Energy Infrastructure.

Abstract

In India, the primary sources of power generation include wind, coal, and solar energy. Among these,solar energy stands out as a more efficient and sustainable option due to its renewable nature, wideavailability, and minimal environmental impact. 

References

Ahmed, Waqas. "Enhancing solar PV reliability with hybrid local features and infrared thermography." Energy Reports 13 (2024): 345-352.

Baltacı, Özge, Zeki Kıral, Konuralp Dalkılınç, and Oğulcan Karaman. "Thermal Image and

Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems." Applied Sciences 14,

no. 9 (2024): 3671.

Downloads

Published

2025-04-15

How to Cite

G. Tejaswi, Gudugunta Kalyani, Chilukuri Damini, Gangapatnam Varshitha. (2025). Machine Learning-Driven Thermal Imaging for Fault Detection in Solar PV Systems. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 1295–1302. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3127

Issue

Vol. 34 No. 4 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.