Aerial identification of flashed over faulty insulator using binary image classification

  • Shafi Muhammad Jiskani Directorate of Postgraduate Studies, Mehran University of Engineering and Technology Jamshoro-76060 Pakistan | Department of Electrical Engineering, Mehran University of Engineering and Technology Jamshoro-76060 Pakistan
  • Tanweer Hussain Department of Mechanical Engineering, Mehran University of Engineering and Technology Jamshoro-76060 Pakistan
  • Anwar Ali Sahito Department of Electrical Engineering, Mehran University of Engineering and Technology Jamshoro-76060 Pakistan
  • Faheemullah Shaikh Department of Electrical Engineering, Mehran University of Engineering and Technology Jamshoro-76060 Pakistan
  • Ali Akbar Shah NCRA-HHR&CM Lab, Mehran University of Engineering and Technology Jamshoro-76060 Pakistan

Abstract

Flashed over insulator faults are the most significant faults in high voltage line insulators. They are complicated to identify using traditional methods due to their labor-intensive nature. This study proposes a deep learning-based algorithm for detecting flashed over insulator faults in the real time. The algorithm is based on the Resnet 50 architecture, which has been shown to be effective for image classification tasks in the previous studies regarding image analysis. The algorithm is fast, robust and efficient, making it suitable for real-time applications. The algorithm is trained on a dataset of images of flashed over and non-flashed over insulators. This dataset was collected from various transmission lines and National Center of Robotics and Automation, which are located in Pakistan. For validating the effectiveness of the Resnet 50 algorithm, it was compared with the results obtained from the two other widely popular deep learning algorithms, Densenet 121 and VGG 16 (trained and validated on the same dataset). The results showed that the Resnet 50 was able to detect flashed over insulator faults with an accuracy of over 99%. Whereas the Densenet 121 and VGG 16 have achieved an accuracy of less than 51%.

Published
Jan 1, 2024
How to Cite
JISKANI, Shafi Muhammad et al. Aerial identification of flashed over faulty insulator using binary image classification. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 43, n. 1, p. 225-233, jan. 2024. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/3006>. Date accessed: 27 feb. 2024. doi: http://dx.doi.org/10.22581/muet1982.2401.3006.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License