The 12th International Conference on Fuzzy Systems and Data Mining (FSDM 2026)

Abstract: To be updated

Abstract: Enhancing the resilience of electricity transmission infrastructure requires timely situational awareness, reliable diagnostics, and actionable early warnings under uncertain and heterogeneous conditions. The presented results are achieved from an integrated research effort focused on digital technologies and analytical models for critical infrastructure monitoring, with emphasis on experimental validation in controlled and pre-operational settings. It covers a modular approach combining remote sensing, field telemetry, operational logs, and physical simulation to support future digital twin and early-warning capabilities.
The presented work is derived from multiple data modalities and demonstrators. Airborne hyperspectral campaigns (VNIR/SWIR) over selected Slovak localities produced a georeferenced and annotated dataset enabling ecological and infrastructure mapping, including a labelled geodatabase of vegetation and non-vegetation structures. Complementary LiDAR point clouds were processed into classified outputs and canopy height models suitable for 3D integration. For condition monitoring, acoustic diagnostics assessed the feasibility of detecting patterns related to high-voltage insulators using large-scale audio recordings and power lines audio embeddings, while telemetric meteorological observations were analysed to identify conditions linked to moisture-driven degradation, and dust contamination. On the operational side, SCADA/RIS logs and PMU phasor measurements were investigated for synchronization, causality inference, and precursor detection using deep learning concepts (e.g., sequence models and graph-based architectures). Finally, fire-risk assessment was explored through Fire Dynamics Simulator scenarios to study vegetation heterogeneity effects and establish methodological foundations for calibration to local conditions.