Fault Identification of PV Cells in Solar Panel Using Reinforcement Learning

Janarthanan S, Vijayachitra S, Keerthanashree T, Neha G, Vikash A, Manjithraja S

2025

Abstract

Electricity demand is increasing day by day and hence power utilities are slowly shifting towards renewable energy, mainly solar, as it is more reliable and environment friendly. However, solar power generation systems have very low efficiency and this is the major challenge faced by the researchers. Some of the reasons for the low efficiency is the presence of dust particles, bird droppings, shadows, rain droplets, microcracks etc. Out of these, microcracks can be avoided if detected on time whereas remaining parameters have to be addressed on a regular basis as they are issues related to environmental factors. Microcracks are mainly due to manufacturing defects as well as improper handling during transportation and installation. Manual inspection of panels to identify microcracks is both challenging and time-intensive, particularly for panels with large dimensions and high power ratings. This proposed work addresses the process of detection of microcracks using an improved technology which detects the crack within very less time as compared to the existing technologies. Reinforcement Learning method is used to detect and classify the solar panel images as either cracked or non-cracked.

Paper Citation

in Harvard Style

S J., S V., T K., G N., A V. and S M. (2025). Fault Identification of PV Cells in Solar Panel Using Reinforcement Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 796-803. DOI: 10.5220/0013602900004664

in Bibtex Style

@conference{incoft25,
author={Janarthanan S and Vijayachitra S and Keerthanashree T and Neha G and Vikash A and Manjithraja S},
title={Fault Identification of PV Cells in Solar Panel Using Reinforcement Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={796-803},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013602900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Fault Identification of PV Cells in Solar Panel Using Reinforcement Learning
SN - 978-989-758-763-4
AU - S J.
AU - S V.
AU - T K.
AU - G N.
AU - A V.
AU - S M.
PY - 2025
SP - 796
EP - 803
DO - 10.5220/0013602900004664
PB - SciTePress