Field-Validated Multi-Source Sensor Fusion Framework for Iced Conductor Galloping Early Warning: A 15-Month 220 kV Demonstration

Iced conductor galloping poses a critical threat to high-voltage transmission line safety and stability, yet existing monitoring systems are constrained by single-sensor dependency, inadequate signal denoising, and limited early warning accuracy. This paper presents a field-validated framework integrating multi-source sensor fusion, adaptive signal denoising, and deep learning-based early warning for iced conductor galloping. A five-layer Internet of Things (IoT) monitoring architecture is designed, fusing fiber Bragg grating (FBG) arrays, MEMS inertial sensors, and micro-meteorological stations, with dual-spectrum cameras providing auxiliary visual verification. Self-powered MEMS nodes utilizing electromagnetic induction energy harvesting are shown to have achieved year-round zero-external-power maintenance. An improved hummingbird local optimization algorithm (IHLOA) adaptively optimizes variational mode decomposition (VMD) parameters, combined with wavelet threshold denoising (WTD) for joint signal preprocessing, achieving a 17.78 dB signal-to-noise ratio improvement [95% CI: 17.2–18.3 dB]. A 26-dimensional multi-domain feature vector is constructed and reduced to 12 discriminative features via ReliefF selection. A Temporal Adaptation Gated Recurrent Unit with Attention (TA-GRU-Attention) model incorporating an adaptive irregular time series perception module is developed for galloping early warning, with all models evaluated exclusively on real-event test samples. Experimental validation through 1:20 scale wind tunnel aeroelastic tests and a 15-month field demonstration on an operating 220 kV transmission line at 2800–3200 m elevation demonstrates a galloping prediction accuracy of 91.3% [95% CI: 81.5–97.2%] under stratified time series split, a missed alarm rate of 12.2%, and a median advance warning time of 38.5 min (range: 18–65 min, IQR: 26–52 min), with 97.3% system availability over the deployment period.