[论文解读] Topology-Aware Spatio-Temporal Graph Transformer for Predicting Smart Grid Failures

A. Lê, Phat K. Huynh+4 more|arXiv (Cornell University)|Jan 6, 2026

Smart Grid Security and Resilience被引用 0

一句话总结

引入一个考虑拓扑的时空图变换器（ST-GT），将物理传输拓扑、静态描述符和PMU序列整合，用于预测智能电网故障，具备完全召回率和较强的F1分数，优于XGBoost基线。

ABSTRACT

Smart grid infrastructure needs improved resilience and preventive maintenance through more accurate predictions. Current methodologies lack accurate representation of spatio-temporal-causal interdependencies and class imbalance in failure prediction tasks. This study introduces a Topology-Aware Spatio-Temporal Graph Transformer (ST-GT) architecture that overcomes existing limitations by using three main innovations: (1) directly incorporating physical transmission network topology into the transformer attention mechanism to identify spatial failure propagation patterns; (2) unified processing of static topological descriptors and (3) temporal Phasor Measurement Units (PMU) sequences in an end-to-end framework. The ST-GT model exhibited outstanding performance in five-fold cross-validation across 10 substations, attaining perfect recall (1.000 $\pm$ 0.001) and an F1-score of 0.858 $\pm$ 0.009, markedly surpassing XGBoost baselines (0.683 accuracy/F1). Perfect recall guarantees that no critical failures are overlooked, which is essential for grid safety; however, it may lead to an increase in false alarm rates. This framework integrates temporal dynamics modeling with spatial graph awareness for critical infrastructure monitoring. It offers interpretable insights into failure propagation pathways and enhances maintenance strategies. Future research focuses on developing cost-weighted loss functions for precision-recall trade-off enhancement, implementing real-time monitoring systems with uncertainty quantification, and creating cost-sensitive frameworks balancing false alarm expenditures with failure consequences. The methodology success suggests its potential for wider application in critical infrastructure areas requiring spatial temporal failure prediction.

研究动机与目标

• 通过精准的故障预测推动智能电网的韧性提升与预防性维护。
• 解决现有方法在表示时空因果相关性与类别不平衡方面的局限。
• 开发一个统一的端到端模型，将拓扑、静态描述符和时序PMU数据融合。
• 为故障传播路径提供可解释的洞见，以指导维护策略。

提出的方法

• 直接将物理传输网络拓扑纳入变换器注意力机制，以识别空间故障传播模式。
• 在一个端到端框架内统一处理静态拓扑描述符和时序PMU序列。
• 采用时空图变换器（ST-GT）架构，对10个变电站的动态进行建模，并进行五折交叉验证。
• 对比基线（如XGBoost）分析性能，采用召回率和F1分数等指标。
• 讨论潜在扩展，包括成本加权损失、带不确定性量化的实时监控，以及成本敏感框架。

实验结果

研究问题

• RQ1 如何将拓扑集成到变换器中，以捕捉电网故障的空间传播模式？
• RQ2 一个统一模型是否能利用静态拓扑描述符和时序PMU数据来提升故障预测？
• RQ3 与传统基线相比，所提ST-GT在智能电网故障任务上的性能如何？

主要发现

• ST-GT在五折交叉验证下，对10个变电站的召回率为1.000 ± 0.001，F1-score为0.858 ± 0.009。
• 该模型显著超过XGBoost基线（0.683的准确率/F1）。
• 该方法为故障传播路径提供可解释的洞见，支持预防性维护决策。
• 完全召回率确保不会漏检关键故障，可能在误警率上存在权衡。
• 未来工作包括成本加权损失、带不确定性意识的实时监控，以及成本敏感部署。