Beyond GCN: Accurate Real-time Inertia Estimation with Multi-Head Spatial-Temporal Graph Attention Network Coupled with LSTM

This paper addresses the increasingly critical challenge of accurate inertia estimation in modern power systems undergoing a significant transition from traditional synchronous generators to renewable energy sources. The high penetration of renewables leads to a reduction in system inertia, causing increased rate of change of frequency (ROCOF) and frequency deviations, which can threaten grid stability. To tackle this issue, we introduce a novel measurement-based hybrid Multi-Head Graph Attention Network and Long Short-Term Memory (GATLSTM) framework for estimating the total inertia constant. Our approach uniquely integrates the spatial awareness of graph attention networks (GATs) to capture the topological dependencies of the power network with the temporal modeling capabilities of Long Short-Term Memory (LSTM) networks to learn from sequential phasor measurement unit (PMU) data. By leveraging PMU measurements of frequency deviation and ROCOF from a dataset based on the IEEE 24-bus system, our experimental results demonstrate that the proposed GATLSTM model significantly outperforms conventional deep learning models such as Graph Convolutional Networks (GCN) particularly under realistic noise conditions. Extensive experiments, sensitivity analysis, and an ambiguity-set evaluation demonstrate the GAT-LSTM's robustness, superior accuracy, and ability to resolve overlapping-signal cases while providing well-calibrated prediction intervals. These results highlight its potential for realtime power system monitoring and enhanced resilience against dynamic system variations and measurement noise.