ProDER：一种用于智能电网故障预测的持续学习方法
ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids
AI总结：
针对智能电网故障预测，提出持续学习框架，设计评估场景，引入ProDER方法，该方法集成多种技术，在测试CL技术中性能最佳，不同场景下故障类型和区域预测准确率下降低，证明资源高效持续学习系统的实用性。
摘要：
随着智能电网不断发展以满足日益增长的能源需求和应对现代运营挑战，准确预测故障的能力变得愈发关键。然而，现有的基于人工智能的故障预测模型在不断演变的环境中难以确保可靠性，因为它们需要适应新的故障类型和运营区域。在本文中，我们提出了一个在智能电网环境下的持续学习（CL）框架，以使模型与环境共同演进。我们设计了四个基于类增量和域增量学习的现实评估场景，以模拟不断变化的电网条件。我们进一步引入了基于原型的黑暗经验回放（ProDER），这是一种统一的基于回放的方法，它集成了基于原型的特征正则化、逻辑蒸馏和原型引导的回放记忆。ProDER在测试的CL技术中取得了最佳性能，在不同场景下，故障类型预测的准确率下降高达0.032，故障区域预测的准确率下降高达0.033。这些结果证明了资源高效的持续学习系统的实用性，该系统减少了在不断发展的能源基础设施中维护智能故障预测服务的计算和存储负担。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
