Self-Powered Vibration Sensor and Monitoring System for Overhead Power Transmission Lines

Overhead transmission lines are continuously exposed to wind-induced aeolian vibration, which may cause conductor fatigue and threaten grid reliability. To overcome the limitations of battery-dependent monitoring approaches, this work proposes a self-powered intelligent vibration monitoring system based on an electromagnetic–triboelectric hybrid nanogenerator (EMO-TENG). Within the system architecture, the electromagnetic generator (EMG) provides harvested energy for intermittent operation, while the triboelectric nanogenerator (TENG) serves as the vibration sensing unit, forming an integrated energy–sensing structure. The EMG achieves a peak output power of 4.2 mW, enabling low-duty-cycle sensing and wireless transmission. To bridge physical sensing and vibration parameter estimation, a physics-informed signal feature extraction strategy is first developed, where dual-peak amplitude and inter-peak interval characteristics are constructed based on the TENG contact–separation mechanism. These features capture intrinsic relationships between electrical output and mechanical vibration states. On this basis, an intelligent inversion framework using an improved group optimization algorithm (IGOA)-assisted CNN–GRU–Attention neural network is introduced. The optimization algorithm performs adaptive hyperparameter search, enhancing nonlinear feature mapping and multi-parameter decoupling capability. Experimental results demonstrate accurate quantification of vibration frequency, acceleration, and amplitude, with R² values exceeding 0.996. By integrating energy harvesting, physics-informed feature construction, and optimized intelligent inversion within a unified architecture, the proposed system provides a scalable and low-maintenance solution for autonomous transmission-line vibration monitoring and contributes a system-level methodology for intelligent electric Internet of Things (eIoT) sensing.