Image Processing Based Hot-Spot Detection on Photovoltaic Panels

Authors

  • S. Gayathri Monicka Professor, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Tamil Nadu, India.
  • D. Manimegalai Research Scholar, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Tamil Nadu, India.
  • M. Karthikeyan Assistant Professor, Department of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Avadi, Tamil Nadu, India.
  • R. Gunasekari Associate Professor, Department of Electrical and Electronics Engineering, Sri Sairam College of Engineering, Bengaluru, India.

Keywords:

PV monitoring system, Image segmentation, Artificial Neural Network (ANN), JSEG Algorithm, Hot-spot

Abstract

Photovoltaic systems have become more popular as people become more interested in developing energy from renewable resources. Even after the installations, however, there is still a lack of understanding about the importance of inspecting the condition of the PV modules. To keep the PV running, early hot-spot detection is required. For detecting hot-spots, thermal imaging is still a popular technique. This research proposes to develop a method for detecting hot-spots in thermal images of photovoltaic modules using artificial intelligence techniques. Pre-processing, segmentation with an Artificial Neural Network and identification are the three main processes. The proposed method appears to be a good choice for improving hot-spot detection in PV monitoring systems, according to the results of the experiments.

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Published

16.04.2023

How to Cite

S. Gayathri Monicka, D. Manimegalai, M. Karthikeyan, & R. Gunasekari. (2023). Image Processing Based Hot-Spot Detection on Photovoltaic Panels. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 510–518. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2812

Issue

Vol. 11 No. 5s (2023): Special Issue on Applications of Advanced Engineering Technologies

Section

Research Article

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