single-jc.php

JACIII Vol.27 No.3 pp. 458-466
doi: 10.20965/jaciii.2023.p0458
(2023)

Research Paper:

A Fault Warning Method for Hotline Tap Clamp Infrared Images Based on Hybrid Segmentation

Peifeng Shen*, Yang Yang**,†, Lihua Li**, Ting Chen*, and Ning Yang**

*Taizhou Power Supply Branch, Jiangsu Electric Power Company
No.2 Fenghuang West Road, Taizhou, Zhejiang 318000, China

**China Electric Power Research Institute
No.15 Xiaoying East Road, Qinghe, Beijing 100192, China

Corresponding author

Received:
December 16, 2022
Accepted:
February 4, 2023
Published:
May 20, 2023
Keywords:
fault warning, image segmentation, CV model, two-dimensional Otsu, Prewitt operator
Abstract

Substation equipment faults are typically related to the heating of equipment components. The hotline tap clamps of substation are critical components for carrying load currents and thermal fault potential. As a result, a new hybrid early warning approach for hotline tap clamp faults in substation equipment is presented. A two-dimensional Otsu algorithm is used to coarse-segment infrared images to minimize the subsequent complexity. Since the Chan–Vese (CV) model is insufficiently accurate for image segmentation with uneven grayscale, then the differential data obtained by the Prewitt operator to identify the goal edges are combined with the CV model to improve segmentation accuracy. The improved CV model achieves excellent segmentation of the hotline tap clamp in the substation. The temperature statistics are utilized for the segmented images, and the hotline tap clamp fault warning is realized based totally on the relative temperature difference. Finally, the experiments exhibit that the method can enhance the segmentation impact of infrared images and obtain the goal of fault warning.

A new hybrid early fault warning method

A new hybrid early fault warning method

Cite this article as:
P. Shen, Y. Yang, L. Li, T. Chen, and N. Yang, “A Fault Warning Method for Hotline Tap Clamp Infrared Images Based on Hybrid Segmentation,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 458-466, 2023.
Data files:
References
  1. [1] H. Chen, “Application and Analysis of Infrared Temperature Measurement Technology in Judging Thermal Fault of Electrical Equipment,” Electronic Test, Vol.19, pp. 95-97, 2019 (in Chinese). https://doi.org/10.16520/j.cnki.1000-8519.2019.19.036.
  2. [2] M. Wang, W. Du, H. Sun, and J. Zhang, “Transmission Line Fault Diagnosis Method Based on Infrared Image Recognition,” Infrared Technology, Vol.39, No.04, pp. 383-386, 2017 (in Chinese).
  3. [3] W. Li, K. Xie, X. Liao, X. Li, and H. Wang, “Intelligent Diagnosis Method of Infrared Image for Transformer Equipment Based on Improved Faster RCNN,” Southern Power System Technology, Vol.13, No.12, pp. 79-84, 2019. https://doi.org/10.13648/j.cnki.issn1674-0629.2019.12.012
  4. [4] X. Xu, P. Qian, Y. Wang, X. Zhou, H. Xu, and L. Xu, “Multi-granularity hazard detection method for electrical power system,” J. of Beijing University of Aeronautics and Astronautics, Vol.47, No.03, pp. 520-530, 2021. https://doi.org/10.13700/j.bh.1001-5965.2020.0491
  5. [5] Z. Yang, Z. Zhang, D. Wen, X. Zhu, X. Li, and Y. Wei, “Research on Image Segmentation and Fault Diagnosis of Infrared Detection in Substation Equipment,” Bulletin of Science and Technology, Vol.35, No.03, pp. 95-99, 2019 (in Chinese). https://doi.org/10.13774/j.cnki.kjtb.2019.03.017.
  6. [6] X. Wang and H. Mao, “Infrared Image Segmentation Method for Power Equipment in Complex Background,” Infrared Technology, Vol.41, No.12, pp. 1111-1116, 2019 (in Chinese).
  7. [7] Y.-C. Chou and L. Yao, “Automatic Diagnosis System of Electrical Equipment Using Infrared Thermography,” 2009 Int. Conf. of Soft Computing and Pattern Recognition, pp. 155-160, 2009. https://doi.org/10.1109/SoCPaR.2009.41
  8. [8] P. Gu and F. Huang, “Power equipment infrared image segmentation based on improved Chan–Vese model,” Computer Engineering and Applications, Vol.53, No.10, pp. 193-196+212, 2017 (in Chinese).
  9. [9] X. Wang, C. Kang, H. Chen, H. Zeng, J. Xin, and Q. Ji, “Automatic Diagnosis Method for Thermal Faults of Substation Equipment Based on Infrared Image Processing,” J. of East China Jiaotong University, Vol.36, No.03, pp. 111-118, 2019 (in Chinese). https://doi.org/10.16749/j.cnki.jecjtu.2019.03.015
  10. [10] J. Chen, L. Xiao, X. Wan, R. Mai, H. Qin, and L. Li, “Research on Enhancement and Segmentation of Power Equipment Infrared Heat Map in Complex Environment,” Infrared Technology, Vol.40, No.11, pp. 1112-1118, 2018 (in Chinese).
  11. [11] T.-H. Tsai, S.-S. Su, and T.-Y. Lee, “Fast Mode Decision Method Based on Edge Feature for HEVC Inter-Prediction,” IET Image Processing, Vol.12, No.5, pp. 644-651, 2018. https://doi.org/10.1049/iet-ipr.2016.1117
  12. [12] S. Hu, “Method and Criterion of Relative Temperature Difference Determination of Infrared Diagnosis for Electric Equipment,” Power System Technology, No.10, pp. 49-52, 1998.
  13. [13] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.9, No.1, pp. 62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  14. [14] Y. Lin and X. Chen, “Fault zone extraction of electrical equipment by using PCNN based on Otsu optimization,” J. of Fujian University of Technology, Vol.18, No.06, pp. 593-597, 2020 (in Chinese).
  15. [15] J.-L. Fan and F. Zhao, “Two-Dimensional Otsu’s Curve Thresholding Segmentation Method for Gray-Level Images,” Acta Electronica Sinica, No.04, pp. 751-755, 2007 (in Chinese).
  16. [16] H.-T. Guo, L.-Y. Wang, T. Tian, C.-T. Zhang, and H.-Q. Sun, “Automatic Thresholding Using the Otsu Algorithm Based on the Two-Dimensional Bound Histogram,” J. of Optoelectronics.laser, No.06, pp. 739-742, 2005 (in Chinese).
  17. [17] T. F. Chan, B. Y. Sandberg, and L. A. Vese, “Active Contours Without Edges for Vector-Valued Images,” J. of Visual Communication and Image Representation, Vol.11, Issue 2, pp. 130-141, 2000. https://doi.org/10.1006/jvci.1999.0442
  18. [18] V. V. Terzija and H.-J. Koglin, “On the Modeling of Long Arc in Still Air and Arc Resistance Calculation,” IEEE Trans. on Power Delivery, Vol.19, No.3, pp. 1012-1017, 2004. https://doi.org/10.1109/TPWRD.2004.829912
  19. [19] Y. Zhang, K. Tang, Y. Liu, X. Bai, and J. Liu, “An Improved Active Contour Model Based on C-V Model,” J. of Sichuan University of Science & Engineering (Natural Science Edition), Vol.27, No.05, pp. 33-36, 2014 (in Chinese).
  20. [20] Z. Pan, Z.-Z. Wang, D. Xu, Y.-C. Shuang, Z.-H. Wang, and Y.-K. Li, “A Method of UV Image Segmentation for Corona Discharge Based on C-V Model,” Science Technology and Engineering, Vol.20, No.14, pp. 5661-5666, 2020.
  21. [21] X. Zhao, P. Zhou, and M. Xue, “A Kind of Infrared Image Segment Method Using Improved Chan–Vese Model,” Infrared Technology, Vol.38, No.09, pp. 774-778, 2016 (in Chinese).
  22. [22] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. of Computer Vision, Vol.22, pp. 61-79, 1997. https://doi.org/10.1023/A:1007979827043
  23. [23] C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. on Image Processing, Vol.7, No.3, pp. 359-369, 1998. https://doi.org/10.1109/83.661186
  24. [24] C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, “Implicit active contours driven by local binary fitting energy,” 2007 IEEE Conf. on Computer Vision and Pattern Recognition, 2007. https://doi.org/10.1109/CVPR.2007.383014

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024