STGATv2模型在高渗透分布式能源下仍能精准定位电网故障，表现优于其他模型。
Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels
- 采用时空图注意力网络联合建模电网空间与时间特征。
- 在50%渗透率下训练的模型仍保持81-84%宏平均F1，远超其他模型。
- 对拓扑结构敏感，适合复杂主动配电网中的故障定位任务。
准确的故障定位对配电网络可靠性至关重要。然而，分布式能源（DER）渗透率升高带来间歇性发电和双向潮流，重塑故障特征，使定位更困难。时空图神经网络（STGNNs）通过联合建模空间与时间依赖关系展现出潜力，但其在高DER渗透下的泛化能力尚未被系统研究。本文（i）系统对比了时空图注意力网络（STGATv2）与纯时序（GRU）、纯空间（GATv2）及传统机器学习基线；（ii）评估模型在重新配置的IEEE 123-节点馈线中，于10%、25%、50%多点分布式能源注入与中高阻抗故障下的泛化性能。结果表明，STGATv2在分布内始终优于神经网络基线，达92-94%宏平均F1。泛化呈现不对称性：50%训练时在低渗透下仍保持近分布内性能，而10%训练时在50%下显著退化——STGATv2仍维持81-84% F1，远高于GATv2（69-74%）与GRU（73-75%）。在真实测量噪声下，STGATv2保持>85% F1，GRU最低降至33.5% F1，凸显拓扑感知在主动配电网中鲁棒故障定位的关键作用。
原文摘要 · Abstract (English)
Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates fault location due to intermittent generation and bidirectional power flows that reshape fault signatures. Spatio-Temporal Graph Neural Networks (STGNNs) have shown promise by jointly modeling spatial and temporal dependencies, but their behavior under increasing DER penetration has not been studied rigorously. In this paper, we (i) systematically benchmark spatio-temporal graph attention network (STGATv2) against purely temporal (gated recurrent unit, GRU), purely spatial (GATv2) and traditional machine learning baselines, and (ii) evaluate how well models generalize across increasing DER penetration levels (10%, 25%, 50%) on a reconfigured IEEE 123-bus feeder with multiple DER injection points and moderate-to-high impedance faults. Results show that STGATv2 consistently outperforms neural baselines, achieving 92-94% macro F1 in-distribution. Notably, generalization across penetration levels is asymmetric: training at 50% penetration retains near in-distribution F1 score at lower levels, whereas training at 10% degrades considerably at 50% - with STGATv2 retaining 81-84% F1 under these drastic shifts, substantially higher than GATv2 and GRU which drop to 69-74% F1 and 73-75% F1 respectively. Under realistic measurement noise, STGATv2 maintains > 85% F1, while GRU drops as low as 33.5% F1, highlighting the critical role of topological awareness for robust fault location in active distribution networks.
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