Triboelectric‐Electromagnetic‐Piezoelectric System for Self‐Powered Aeolian‐Vibration Sensing and Suppression on Overhead Lines

ABSTRACT This study develops a hybrid triboelectric‐electromagnetic‐piezoelectric generator for overhead‐line applications. Integrating a multilayer spring‐mass TENG (MS‐TENG), a vertical‐reciprocating EMG (VR‐EMG), and an anti‐vibration PEG (AV‐PEG), the device unifies energy collection, vibration sensing, and adaptive damping within one platform. Operating effectively in the 5–40 Hz aeolian‐vibration band, the three modules deliver peak outputs of 272.8 V/31.2 µA/3.3 mW, 16.2 V/26.1 mA/59.7 mW, and 61.7 V/0.83 mA/9.8 mW, respectively. A triple‐port power‐management circuit (PMC) regulates this multi‐source input, supplying stable continuous DC power to sensor nodes. For condition identification, time‐domain signals from the MS‐TENG are transformed into frequency‐domain features via Fast Fourier Transform (FFT). Conductor vibration is divided into nine classes based on frequency and amplitude. A convolutional neural network (CNN) is trained to associate external vibration excitations with triboelectric outputs. Embedded in a microcontroller, this trained model creates a compact, self‐powered vibration‐assessment and hierarchical early‐warning system. Experiments under simulated transmission‐line aeolian vibration verify accurate real‐time state recognition and reliable multi‐level alerts. Ultimately, this work offers an integrated, self‐sufficient strategy for intelligent condition recognition and adaptive vibration protection in high‐voltage infrastructure.