Real-time measurement data synchronization and quality monitoring of the global main distribution network based on temporal fusion transformer and EdgeGNN

Abstract Real-time measurement data synchronization and quality monitoring in global power main-distribution networks face challenges of dynamic topology shifts, heterogeneous communication delays, and frequent data anomalies. To address this, we propose a hybrid deep learning framework integrating spatiotemporal features. First, a Temporal Fusion Transformer (TFT) extracts multi-granularity temporal features across hourly, daily, and weekly windows. Next, an Edge-enhanced Graph Neural Network (EdgeGNN) embeds spatial topology using edge attributes (e.g., line impedance, status). Finally, a dynamic gating mechanism fuses spatiotemporal representations and adapts to grid reconfigurations. Experiments demonstrate average synchronization delays of 26.7-31.5 ms, anomaly detection F1-scores up to 0.89, and stable quality assessment response at 30.7 ms, validating the framework’s superiority in accuracy and robustness for grid data governance.