评估图神经网络在分布式能源渗透率不断提升场景下的故障定位泛化能力
Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels
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中文总结 AI 辅助
本文以配电网故障定位为研究问题，通过系统基准测试STGATv2与GRU、GATv2等模型，发现STGATv2在不同DER渗透率场景下的泛化能力及抗噪声性能显著优于基线模型，为主动配电网故障定位提供了鲁棒方案。
中文摘要 AI 辅助
准确的故障定位对配电网可靠性至关重要，但分布式能源（DER）渗透率的不断提升会因间歇性发电和重塑故障特征的双向潮流使故障定位变得复杂。时空图神经网络（STGNNs）通过联合建模时空依赖关系展现出应用前景，但其在DER渗透率不断提升场景下的表现尚未得到严格研究。本文开展两项工作：一是系统将时空图注意力网络（STGATv2）与纯时序（门控循环单元，GRU）、纯空间（GATv2）及传统机器学习基线模型进行基准测试；二是在重构的含多个DER注入点及中高阻抗故障的IEEE 123节点馈线系统上，评估模型在10%、25%、50%这几个不断提升的DER渗透率水平间的泛化能力。结果显示，STGATv2始终优于其他神经基线模型，在分布内场景下达到92%-94%的宏F1值。值得注意的是，不同渗透率水平间的泛化能力呈非对称特性：在50%渗透率下训练的模型，在更低渗透率水平下仍能保持接近分布内的F1分数；而在10%渗透率下训练的模型，在50%渗透率下性能大幅下降，此时STGATv2仍能保持81%-84%的F1值，显著高于GATv2（降至69%-74%）和GRU（降至73%-75%）。在实际测量噪声条件下，STGATv2仍能保持>85%的F1值，而GRU的F1值低至33.5%，凸显拓扑感知对主动配电网中故障定位鲁棒性的关键作用。
英文摘要
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.
