A STUDY ON AUTOMATIC IDENTIFICATION AND COUNTING FREQUENCY ANALYSIS OF TRANSMISSION LINE DANCING FEATURES BASED ON DEEP LEARNING

RAN JIA; CHAO ZHOU; HUI LIU; CHUANBIN LIU; HUA LIU

doi:https://doi.org/10.52152/D11381

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A STUDY ON AUTOMATIC IDENTIFICATION AND COUNTING FREQUENCY ANALYSIS OF TRANSMISSION LINE DANCING FEATURES BASED ON DEEP LEARNING

SEPTEMBER 2025 - Volume: 100 - Pages: 422-428

DOI:

https://doi.org/10.52152/D11381

Authors:

RAN JIA - CHAO ZHOU - HUI LIU - CHUANBIN LIU - HUA LIU

Disciplines:

Electrical technology and engineering (TRANSMISION Y DISTRIBUCION )

Downloads: 8

How to cite this paper:

Received Date : 5 December 2024

Reviewing Date : 15 January 2025

Accepted Date : 5 May 2025

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Key words:: Transmission line; Deep learning; Neural network model; Dance characteristics; Counting frequency; Streaming media; Gamma correction; Convolutional layer; Video image segmentation; MRF; Edge point; CBMA
Article type:: ARTICULO DE INVESTIGACION / RESEARCH ARTICLE
Section:: RESEARCH ARTICLES

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.
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

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