A study on automatic identification and counting frequency analysis of transmission line dancing features based on deep learning

Authors

  • Ran Jia 1 State Grid Shandong Electric Power Research Institute - 50002 Jinan (China) Author
  • Chao Zhou 1 State Grid Shandong Electric Power Research Institute - 50002 Jinan (China) Author
  • Hui Liu State Grid Shandong Electric Power Research Institute - 50002 Jinan (China). Author
  • Chuanbin Liu State Grid Shandong Electric Power Research Institute - 50002 Jinan (China) Author
  • Hua Liu Institute of Energy Sensing and Information. Sichuan Energy Internet Research Institute. Tsinghua University - 610000 Chengdu (China). Author

DOI:

https://doi.org/10.52152/1sx3tx91

Keywords:

Transmission line; Deep learning; Neural network model; Dance characteristics; Counting frequency; Streaming media; Gamma correction; Convolutional layer; Video image segmentation; MRF; Edge point; CBMA.

Abstract

Transmission line galloping—wind-induced conductor vibrations—threaten grid safety by damaging components and triggering outages. An AI-driven method using HD cameras captures galloping video, which is frame-segmented via DirectShow and clarified with adaptive gamma correction. A Markov random field model isolates conductors/spacers (foreground) from backgrounds, and DeepLabv3+ extracts spatial features for displacement/angle quantification and frequency analysis. Experimental results confirm robust preprocessing, accurate component segmentation, and reliable galloping pattern detection, enabling proactive maintenance and grid resilience.

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Published

2025-09-15

Issue

Vol. 100 No. 5 (2025)

Section

Research articles

License

Copyright (c) 2025 DYNA

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