Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels

AI 中文解读

研究了AI如何在不同新能源接入比例下精准定位电网故障。核心亮点是：一种融合电网空间拓扑和时间变化的智能模型，在新能源渗透率剧烈变化时依然保持高准确率，比传统AI更可靠。通俗来说，现在屋顶光伏、风电越来越多，电力方向不再单一，一旦线路出故障，找起来特别麻烦。研究人员用了一个“会看地图”的AI，它能同时记住电网的接线结构和电流随时间的变化，就像既懂路况又懂导航的老司机。实验显示，这个AI在新能源占比从10%跳到50%时，准确率仍然有八成以上，而普通AI会明显“懵掉”，甚至跌到七成以下。如果测量时有干扰噪音，普通AI准确率可能只剩三成多，但这个模型还能保持八成五以上。实际影响是，未来电网能更快速、准确地发现故障点，减少停电时间，尤其对安装了光伏的家庭和依赖稳定供电的工厂，体验会有实实在在的提升。同时，这也意味着新能源占比越高，电网的智能化维护越需要这种“懂得网络结构”的AI，而不是只看数据点。

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.