Bus-Centric Temporal Graph Neural Network Framework for Fault Localization and- Risk Profiling Using Pmu Time Series Data

In modern power systems, rapid and accurate detection of dynamic events at the bus level is critical for ensuring grid reliability and operational resilience. The paper proposes a novel bus-centric event detection framework that integrates a Temporal Graph Neural Network (TGNN) with unsupervised clustering and supervised classification for event localization and risk assessment using Phasor Measurement Unit (PMU) data. A rich set of twelve statistical and dynamic features per bus including voltage/frequency derivatives, phase angle trends, and rolling statistics is extracted to represent temporal-spatial behaviour withing power grid nodes. a buswise risk index is formulated by combining voltage, frequency, and angle deviations with regression-based R² stability trends, enabling the ranking of critical buses. Furthermore, the performance of the proposed framework will be rigorously evaluated using a comprehensive classification metrics, including Accuracy, F1-score and Matthews Correlation Coefficient (MCC). The proposed framework offers a scalable and interpretable solution for real-time contingency analysis, with strong implications for preventive control in smart grids.