电力系统安全研究者终于有了一个可复现的延迟感知基准——它揭示了AI模型在逆变器主导电网中实时分类故障与攻击时的实际性能瓶颈。做电力系统保护或AI部署的团队可以直接参考这些数据来优化模型和硬件选择。
该研究提出了一个延迟感知的基准框架，用于评估深度学习模型在电力系统异常检测中的表现。研究使用工业级电磁暂态模拟器生成的高保真时域信号，系统评估了从MLP到Transformer的八种神经网络架构。所有模型都能在15毫秒内实时分类多事件序列，但端到端推理延迟在50-90毫秒之间，超过了保护级部署的要求。结果表明算法能力与实际部署之间存在关键差距，需要进一步优化和硬件加速。该工作为亚周期异常检测建立了可复现的基准，指导机器学习方法从研究原型向实际保护应用过渡。
Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids
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.