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.

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