arXiv cs.AI· Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze, Gian Antonio Susto, Chuadhry Mujeeb Ahmed, Mirco Rampazzo·· 3 天前AI 评分20
ProDER：面向演进智能电网故障分类与定位的持续学习方法
ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids
AI 导读
ProDER 将智能电网故障类型分类与故障区域定位建模为持续学习问题，在 IEEE 13 节点测试馈线上设计三个类增量与一个域增量场景，平均准确率达 58.2%，比最强对比方法 DPDMR（51.6%）高 6.6 个百分点，仅比联合训练（61.4%）低 3.2 个百分点。
正文 · 原文
Abstract:Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and monitoring is extended to new grid zones. Retraining from scratch on all accumulated data is costly, while naively updating the model on new data causes catastrophic forgetting. To address this problem, we formulate fault type classification and fault zone localization as continual learning (CL) problems and design four evaluation scenarios on the IEEE 13-node test feeder, three class-incremental and one domain-incremental. We then propose Prototype-based Dark Experience Replay (ProDER), which extends DER++ with prototype attraction and prototype-level repulsion losses that stabilize the feature space, temperature-scaled logit distillation, and a prototype-aware replay memory that retains both core and boundary samples of each class. ProDER achieves the highest accuracy among the tested CL methods in all scenarios, with an average accuracy of 58.2%, 6.6 points above the strongest competing method (DPDMR, 51.6%) and only 3.2 points below joint training (61.4%). Per scenario, it improves over the strongest competitor by 4.2 to 7.4 points and closes the gap to joint training to as little as 1.0 point in fault type classification, while matching it in fault zone localization. Moreover, it remains the best method when the replay buffer is substantially reduced. These results show that prototype-guided replay is an effective, memory-bounded way to keep fault diagnosis models up to date as the grid evolves, while validation on field measurements remains a necessary next step.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2511.05420 [cs.LG] |
| (or arXiv:2511.05420v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2511.05420 arXiv-issued DOI via DataCite |
Submission history
From: Davide Dalle Pezze [view email]
[v1]
Fri, 7 Nov 2025 16:51:32 UTC (9,299 KB)
[v2]
Mon, 20 Jul 2026 15:30:53 UTC (5,441 KB)
[v3]
Mon, 5 Oct 2026 10:06:01 UTC (2,071 KB)
[v4]
Mon, 12 Oct 2026 10:08:48 UTC (1,994 KB)
来源：arXiv cs.AI · arxiv.org