为电力系统故障与攻击检测构建低延迟模型评测框架，揭示算法与实际部署间的性能差距。
Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids
- 基于真实电磁暂态仿真数据，评估八类神经网络在实时流式信号下的表现
- 所有模型实现亚周期响应（<15ms），但端到端延迟达50-90ms超三周期
- 为保护级部署提供可复现基准，适合关注工业应用落地的从业者
本文提出一种面向电力系统异常检测的延迟感知评测框架，利用工业级电磁暂态仿真器生成的高保真时域信号，对从MLP到Transformer的八种神经网络架构进行系统评估。在代表逆变器主导电网中物理故障与网络攻击的流式数据集上，所有模型均实现亚周期响应（低于15毫秒）的实时分类。尽管分类决策可在单个周期内完成，但端到端推理延迟始终超过三个周期，范围为50至90毫秒。结果揭示了算法能力与保护级部署需求之间的关键差距，凸显进一步优化与硬件加速的必要性。研究建立了可复现的亚周期异常检测基准，为机器学习方法从原型向实际保护应用转化提供指导。
原文摘要 · Abstract (English)
This work introduces a latency-aware benchmarking framework for evaluating deep learning models in power system anomaly detection using high-fidelity, time-domain signals generated from an industry-grade electromagnetic transient simulator. Eight neural network architectures, ranging from MLPs to Transformers, were systematically evaluated on streaming datasets representing both physical faults and cyber-attacks in inverter-dominated networks. All models successfully classified two representative multi-event sequences in real time with sub-cycle response times below 15 ms. However, although classification decisions occurred within one cycle, the end-to-end inference latency consistently exceeded three cycles, ranging from 50 to 90 ms. These results highlight a critical gap between algorithmic capability and protection-grade deployment, pointing to the need for further optimization and hardware acceleration. The findings establish a reproducible benchmark for sub-cycle anomaly detection and provide guidance for transitioning machine learning methods from research prototypes to real-world protection applications.
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