提出新方法让电网故障预测模型持续学习新故障类型
ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids
- 用原型驱动的回放机制融合特征正则与逻辑蒸馏
- 在新故障类型和区域下准确率下降不超过3.3%
- 适合需要长期维护的智能电网故障预警系统
随着智能电网为满足不断增长的能源需求和现代运营挑战而演进，精准预测故障变得愈发关键。然而，现有基于AI的故障预测模型在需适应新故障类型和运行区域的动态环境中难以保证可靠性。本文提出一种面向智能电网场景的持续学习（CL）框架，使模型随环境同步演进。设计了四种基于类别增量和领域增量学习的真实评估场景以模拟电网演变条件。进一步提出原型驱动的暗经验回放（ProDER），一种统一的基于回放的方法，整合了原型特征正则化、逻辑蒸馏和原型引导的回放记忆。ProDER在所测试的CL技术中表现最佳，故障类型预测准确率下降最多0.032，故障区域预测下降最多0.033。结果表明，该资源高效的持续学习系统可显著降低维护智能故障预测服务所需的计算与存储开销，适用于演进型能源基础设施。
原文摘要 · Abstract (English)
As smart grids evolve to meet growing energy demands and modern operational challenges, the ability to accurately predict faults becomes increasingly critical. However, existing AI-based fault prediction models struggle to ensure reliability in evolving environments where they are required to adapt to new fault types and operational zones. In this paper, we propose a continual learning (CL) framework in the smart grid context to evolve the model together with the environment. We design four realistic evaluation scenarios grounded in class-incremental and domain-incremental learning to emulate evolving grid conditions. We further introduce Prototype-based Dark Experience Replay (ProDER), a unified replay-based approach that integrates prototype-based feature regularization, logit distillation, and a prototype-guided replay memory. ProDER achieves the best performance among the tested CL techniques, with accuracy drops of up to 0.032 for fault type prediction and up to 0.033 for fault zone prediction across different scenarios. These results demonstrate the practicality of resource-efficient continual learning system that reduces the computational and storage burden of maintaining intelligent fault prediction services in evolving energy infrastructure.
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