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Predicting Voltage Stability Using Graph Convolutional Networks
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DOI：10.1155/je/8588270.png)
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Abstract
En 中文
Real-time voltage stability assessment is critical for secure power grid operation, yet traditional simulation-based methods suffer from high computational latency. While machine learning offers a promising alternative, standard models often fail to leverage the inherent topology and physics of the power grid. This paper addresses this gap by proposing a novel graph deep learning framework to predict the fast voltage stability index (FVSI). The key technical contributions of this work are threefold: (1) the introduction of a novel physics-informed graph attention network (PI-GAT), where the attention mechanism is explicitly guided by physical line impedance to enhance model interpretability and accuracy; (2) the first formulation of FVSI prediction as a spatiotemporal graph learning problem, accompanied by a comprehensive benchmark of architectures, including temporal GCN (TGCN); and (3) the integration of a dropout-based framework to provide practical, real-time uncertainty quantification for predictions. Evaluations on the IEEE 14-bus system demonstrate that the proposed graph-based models are significantly more accurate and robust than classical baselines. This framework offers a scalable, interpretable, and uncertainty-aware solution for real-time voltage monitoring.
Keywords:
fast voltage stability index (FVSI)
graph neural networks (GNNs)
machine learning in power systems
physics-informed graph attention network (PI-GAT)
temporal GCN (TGCN)
uncertainty quantification
voltage stability assessment
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