Infrared Thermal Images of Solar PV Panels for Fault Identification Using Image Processing Technique

Author:

Kirubakaran V.1ORCID,Preethi D. M. D.2,Arunachalam U.3,Rao Yarrapragada K. S. S.4,Gatasheh Mansour K.5,Hoda Nasrul6,Anbese Endalkachew Mergia7ORCID

Affiliation:

1. Centre for Rural Energy, The Gandhigram Rural Institute, Gandhigram, Dindigul, Tamilnadu, India

2. Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

3. Department of Mechanical Engineering, University College of Engineering-Nagercoil, Tamilnadu, Nagercoil, India

4. Department of Mechanical Engineering, Aditya College of Engineering, Surampalem, 533437 Andhra Pradesh, India

5. Department of Biochemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia

6. Department of Neurology, Henry Ford Health System, Detroit MI 48292, USA

7. Department of Civil Engineering, Ambo University, Ambo, Ethiopia

Abstract

Among the renewable forms of energy, solar energy is a convincing, clean energy and acceptable worldwide. Solar PV plants, both ground mounting and the rooftop, are mushrooming thought the world. One of the significant challenges is the fault identification of the solar PV module, since a vast power plant condition monitoring of individual panels is cumbersome. This paper attempts to identify the panel using a thermal imaging system and processes the thermal images using the image processing technique. An ordinary and thermal image has been processed in the image processing tool and proved that thermal images record the hot spots. Similarly, the new and aged solar photovoltaic panels were compared in the image processing technique since any fault in the panel has been recorded as hot spots. The image recorded in the aged panels records hot spots, and performance has been analyzed using conventional metrics. The experimental results have also been verified.

Funder

King Saud University

Publisher

Hindawi Limited

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,Atomic and Molecular Physics, and Optics,General Chemistry

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1. An approach based on deep learning methods to detect the condition of solar panels in solar power plants;Computers and Electrical Engineering;2024-05

2. Detection of Suboptimal Conditions in Photovoltaic Systems Integrating Data from Several Domains;Communications in Computer and Information Science;2024

3. Fusion Colour Model for Photovoltaic (PV) Segmentation;Lecture Notes in Electrical Engineering;2024

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5. A Review on Detection of Solar PV Panels Failures Using Image Processing Techniques;2023 24th International Middle East Power System Conference (MEPCON);2023-12-19

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