An AI-driven approach demonstrates reliable detection of galloping patterns in transmission lines, suggesting enhanced grid safety.
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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Jia et al. (2025) studied this question.
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