Vibration noise reduction of transmission lines based on fiber-optic array by using TOC-ICEEMDAN

Abstract

To address the issue in which abnormal vibration monitoring signals from fiber-optic composite overhead ground wire (OPGW) transmission lines in complex field environments exhibit low signal-to-noise ratios (SNRs) and pronounced nonstationary characteristics, we proposes a tornado optimizer with Coriolis force improved complete ensemble empirical mode decomposition with adaptive noise (TOC-ICEEMDAN) method for fiber-optic sensor arrays. By leveraging the global optimization capability of the TOC and simulating multiscale fluid dynamic mechanisms including Coriolis force effects, the proposed method determines critical ICEEMDAN parameters adaptively. The coupling not only yield improved parameter settings but also produces a restructured decomposition that more accurately reflects the signal’s nonstationary characteristics and modal mixing, thereby enabling more effective separation of weak anomalies from complex background noise. Field validation in wind-induced vibration and sandstorm environments at an OPGW line demonstrates the superior performance of TOC-ICEEMDAN across three core metrics: energy error (EE), average correlation coefficient (ACC), and SNR. Statistical comparisons using paired sample t -tests reveal significant differences between TOC-ICEEMDAN and three benchmark methods. Relative to VMD, the proposed algorithm changes EE by 146.65 ( d = -3.664, p < 0.001), ACC by 0.18 ( d = -3.000, p < 0.001), and SNR by 1.10 dB ( d = 3.633, p < 0.001). Compared with EEMD, EE decreases by 159.81 ( d = -3.996, p < 0.001) while SNR increases by 0.24 dB ( d = 0.800, p = 0.007). Compared with ICEEMDAN, ACC reduction reaches 0.10 ( d = -1.500, p < 0.001). The proposed algorithm significantly enhances weak anomaly detection capability, and successfully extracts abnormal vibration periods across diverse operational conditions, thereby providing an efficient and robust solution for OPGW line monitoring in complex environments.