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| Research on Self-Powered Sensing of Aeolian Vibration State of Transmission Lines |
| Wang Shengjia, Gao Sihang, Liu Yongxi, Hou Jie, Long Lingjiang |
| Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China |
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Abstract With the rapid development of China's power transmission network, the problem of aeolian vibration of power transmission lines with large spans and high suspensions has become increasingly prominent. Long-term accumulation of aeolian vibration can exacerbate the fatigue damage of conductors, and may even lead to strand breakage and wire breakage, seriously threatening the safety and reliability of the power grid. Currently, the monitoring and early warning of aeolian vibration in power lines are mainly carried out through numerical calculations, image acquisition, optical fiber sensors, and various vibration sensors. However, most of the existing vibration monitoring technologies suffer from problems such as insufficient sensing accuracy, large volume of sensing nodes, inability to obtain long-term stable power supply, and inconvenience for flexible deployment.
In this paper, aiming at the characteristics of aeolian vibration in power transmission lines, this paper utilizes environmental micro-energy harvesting technology, a flexible multi-layer triboelectric-electromagnetic hybrid vibration energy harvesting principle is proposed, and the design of a vibration energy harvester is completed based on this principle. Through experimental research, its structural parameters and dielectric films are comprehensively optimized to achieve efficient electromechanical energy conversion and high-sensitivity response within the broadband of aeolian vibration. Furthermore, by combining self-powered sensing technology with deep learning methods, considering the frequency and amplitude ranges of aeolian vibration in power transmission lines and the fatigue damage degree of conductors under different aeolian vibrations, the aeolian vibration of power transmission lines is divided into 9 vibration states. The output electrical signals of the vibration energy harvester are used to characterize the dynamic process triggered by aeolian vibration, and a mapping relationship of “vibration excitation and self-powered sensing characteristics” based on a convolutional neural network is constructed to achieve accurate identification and graded early warning of aeolian vibration states.
The results show that the composite vibration energy harvester designed in this paper has efficient electromechanical energy conversion and high-sensitivity response under the condition of aeolian vibration of conductors. It has excellent electrical output performance in the vibration range of amplitude 1~6 mm and vibration frequency 5~40 Hz. The maximum open-circuit voltage peak-to-peak values of the triboelectric nanogenerator and electromagnetic generator reach 490 V and 17 V respectively, the maximum unilateral values of short-circuit ourrent reach 27 µA and 26 mA respectively, and the peak instantaneous powers reach 4.4 mW and 53 mW respectively. A self-powered vibration state recognition model based on deep learning is proposed in this paper, and the recognition accuracy of aeolian vibration states reaches 97.8%. On this basis, a self-powered sensing system for aeolian vibration in power transmission lines, which integrates a composite vibration energy harvester, a multi - source power management circuit, and a visual upper-computer, is constructed to identify different vibration states and conduct graded early warning, providing a new solution for self-powered sensing and aeolian vibration state monitoring in high-voltage power transmission environments.
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Received: 19 June 2025
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2026, Vol. 41 
