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Research on Transmission Line Acoustic Monitoring Technology Based on Distributed Acoustic Sensing
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DOI：10.1109/tim.2026.3716506.png)
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Abstract
En 中文
Monitoring acoustic signals in the vicinity of transmission lines can effectively identify bird activities, mechanical construction, and lightning events within transmission corridors, providing support for bird hazards and external damage early warning, as well as lightning localization. Through theoretical modeling combined with simulation experiments, this study proposes an acoustic monitoring method for transmission lines based on optical ground wire (OPGW) and distributed acoustic sensing (DAS). First, by establishing models for acoustic wave propagation, acoustic-induced vibration, strain transfer, and DAS acoustic sensing, a quantitative theoretical model and the correlation between far-field acoustic source signals and optical fiber phase parameters are constructed. Second, a transmission line simulation test platform was built to experimentally investigate the acoustic sensing performance of OPGW. This validates the effectiveness of the proposed theoretical model, quantifies the acoustic pressure sensitivity (APS) and frequency response characteristics of the optical cable, and explores the impact of environmental wind speed on its acoustic sensing performance. On this basis, combined with field tests on a 220 kV transmission line, the applicability of the model to actual line scenarios is further discussed. Finally, the accuracies of different deep learning models in the task of transmission line bird song signal recognition are comparatively analyzed. The experimental results indicate that the phase response of the optical cable decreases rapidly with the increase of the acoustic source frequency. The APS is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 109.67~\pm ~0.68$ </tex-math></inline-formula> dB re 1 rad/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Pa under a 100 Hz signal excitation, and drops to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 149.72~\pm ~2.30$ </tex-math></inline-formula> dB re 1 rad/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Pa at 5000 Hz. Furthermore, utilizing deep learning models can effectively identify bird activities on transmission lines, with the maximum recognition accuracy reaching 96.83 %. This study provides a theoretical foundation and experimental validation for distributed acoustic state perception in transmission corridors.
Keywords:
Distributed acoustic sensor
frequency response range
optical ground wire (OPGW)
transmission line
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W
Organization
Cited Papers