Vibration detecting for transmission line based on video monitoring

Tang, Yiming; Zhao, Jianpeng; Li, Jia; Kong, Wenjie; Lin, Xinhua; Yan, Bo; Ni, Pengcheng; Zhao, Jun; Wu, Qinglin

doi:10.1117/12.2585610

Cited by 3 publications

(1 citation statement)

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“…Currently, transmission line monitoring methods primarily include manual inspection, unmanned aerial vehicle (UAV) surveillance, infrared thermal imaging, and fiber optic sensing technologies. Tang [5] proposed a technical approach utilizing video surveillance systems for vibration analysis of transmission lines. This method involves the collection of video image data from transmission lines and the establishment of a relative coordinate system between the power tower and the transmission line, thereby enabling the tracking of vibration frequency and amplitude at specific line positions.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network

YIN,

YU,

WANG

2024

IJACSA

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Given wireless sensor networks' significant data transmission requirements, conventional direct transmission often leads to bandwidth constraints and excessive network energy consumption. This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor networks to achieve real-time monitoring of ice-covered power lines. Grounded in compressed sensing theory, this method utilizes dual orthogonal wavelet transform sparse matrices for sparse representation of sensor data. Considering the practical requirements of power line monitoring, a data transmission model is established to implement compressed sampling transmission. The regularization orthogonal matching pursuit algorithm is employed for high-precision reconstruction of compressed data. The software and hardware components of the power line monitoring system are designed, and experiments are conducted under real-world conditions. The results demonstrate that: 1) the system operates stably with an ideal data compression effect, achieving a compression ratio of 93.191%. The absolute reconstruction errors for temperature, humidity, and wind speed sensor data are 0.064°C, 0.052%, and 0.128 m/s, respectively, indicating high reconstruction accuracy and effectively avoiding transmission impacts caused by bandwidth issues. 2) In a 36-hour energy consumption loss test, compared to direct transmission, the compressed transmission mode exhibits a lower rate of battery voltage decay, with a decrease of approximately 11.18%, effectively extending the network's lifespan.

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Section: Introductionmentioning

confidence: 99%