An effective approach to deal with the structural failures of transmission towers in tornadic events is to develop good structural health monitoring (SHM) systems for them. However, the strategy for SHM of transmission towers against tornados should be different from the conventional atmospheric boundary layer (ABL) winds oriented ones, as the non-stationary nature of the tornados significantly differentiates them from the ABL winds. To satisfy the need of obtaining the highly time-varying whole-field stress on the structure in the course of the tornadic event for effective SHM, an innovative transient tornadic load distribution identification method is proposed for use, which is based on the field structural mode shape measurement and dynamic finite-element (FE) model updating. Via a numerical case study, it is noted that good effectiveness is achieved for the new load distribution identification method. Employing the Modal Assurance Criteria based FE model updating technique, the new method has the advantage of being easily embraced in practical SHM systems. It is found that when the transient tornadic velocity profile to be identified is noticeably different from the mode shape of the structure without undertaking external loads, the identified load pattern is very accurate for the new approach.

Galloping of overhead transmission lines is a low-frequency, large-amplitude vibration hazard that poses a severe threat to power grid safety, yet existing monitoring approaches fail to simultaneously provide flexible deployment, quantitative measurement, and robustness under severe weather conditions. This paper makes three primary contributions. First, we propose a novel line-structure center adsorption algorithm that converts a single operator touch-point into a sub-pixel-precision conductor prompt, achieving prompt accuracy above 95% with one round of interactive correction. Second, we introduce—for the first time—SAM2’s streaming memory architecture for continuous zero-shot pixel-level tracking of galloping conductors under complex outdoor backgrounds including snow, ice, and poor illumination, achieving a segmentation IoU of 93.8% and zero identity switches over 500 consecutive frames, outperforming XMem (87.4%) and DeAOT (88.9%). Third, we develop a two-stage spatial correction framework combining vanishing-point-based inverse perspective mapping (IPM) with equidistant linear transformation (ELT), which eliminates perspective distortion inherent in non-orthogonal field imaging and enables quantitative measurement of galloping amplitude (error < 0.5 m), frequency (error < 0.1 Hz), and inter-phase spacing (ranging error < 1 m). The complete pipeline is implemented on a portable, tripod-mounted device (≤15 kg) integrating a monocular camera, laser rangefinder, and high-precision PTZ gimbal. Field validation at three 110/500 kV sites in Jiangsu Province under extreme winter conditions ($-4$ °C, Level 5 wind, continuous snowfall) confirms engineering-grade accuracy and practical robustness, providing a viable technical pathway for real-time non-contact galloping monitoring and disaster early warning.

Galloping of iced transmission lines under complex microclimates poses a severe threat to power grid security, whereas traditional full-span de-icing strategies suffer from excessive energy redundancy and limited spatial precision. To address the spatial uncertainty of actual ice accretion, a three-dimensional nonlinear aeroelastic finite element model is established by considering geometric nonlinearity and eccentric ice-induced added stiffness. A state-space Monte Carlo framework is then used to evaluate the galloping probability under different wind speed regimes and spatially non-uniform ice distributions. The results reveal a distinct non-monotonic instability characteristic: the galloping probability decreases to 33.0% at 8.0 m/s, forming a clear probability trough and indicating an aerodynamic self-stabilization effect associated with the shift in the baseline effective angle of attack. To map spatial ice heterogeneity to global dynamic instability, a galloping sensitivity index (GSI) based on the Spearman rank correlation coefficient is proposed to identify the dominant sensitive sections responsible for inducing galloping-prone responses. Based on this index, a GSI-guided targeted ultrasonic de-icing decision strategy is constructed. Under the assumption of identical rated power for each section, the proposed strategy activates only 40% of the physical sections and reduces the number of activated sections, as well as the associated operational energy demand, by 60% compared with the full-span de-icing strategy. This framework provides a quantitative basis for linking stochastic ice distribution, galloping probability evaluation, and energy-efficient targeted de-icing decisions.