发表机构
Georgia Institute of Technology; King Fahd University of Petroleum and Minerals（佐治亚理工学院; 法赫德国王石油与矿业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
在PROTECT-90数据集上，用一维CNN和MLP评估单周期故障分类与线路识别，完全观测下准确率近饱和，降级观测下电流优于电压，表明限制因素是测量信息而非架构。
AI 中文摘要
开放电磁暂态数据集开始使基于学习的可复现保护研究成为可能，但这些数据集的实际应用仍需定义时序、感知和验证假设的应用级基准。本文针对近期发布的PROTECT-90数据集，提出了一个面向两个保护任务的初始应用基准：故障类型分类和离散故障线路识别。采用紧凑的一维卷积神经网络（CNN），在严格的逐事件划分下，使用0.25、0.5、1和2周期的起始后窗口进行评估。同时训练了一个非卷积的多层感知机（MLP）作为架构控制基线。结果表明，在完全可观测性下，两个任务几乎饱和，单周期测试准确率分别为故障类型99.84%和线路识别100.00%。主要性能变化出现在降级可观测性下：仅电流输入保持线路识别准确率为100.00%，而仅电压输入将线路识别准确率降至CNN的53.09%和MLP的50.57%。这表明限制因素是测量信息而非神经架构。额外的分层检查显示，在不同拓扑状态和故障电阻区间下性能稳定，而CPU推理仅贡献0.528毫秒至单周期总决策时间20.53毫秒。
英文摘要
Open electromagnetic-transient datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these datasets still requires application-level benchmarks that define timing, sensing, and validation assumptions. This paper presents an initial application benchmark on the recently released PROTECT-90 dataset for two protection-oriented tasks: fault-type classification and discrete faulted-line identification. A compact one-dimensional convolutional neural network (CNN) is evaluated using post-inception windows of 0.25, 0.5, 1, and 2 cycles under strict episode-wise splitting. A non-convolutional multilayer perceptron (MLP) is also trained as an architecture-control baseline. The results show that both tasks are nearly saturated under full observability, with one-cycle test accuracies of 99.84% for fault type and 100.00% for line identification. The main performance variation appears under reduced observability: current-only inputs preserve line identification accuracy at 100.00%, whereas voltage-only inputs reduce line identification accuracy to 53.09% with the CNN and 50.57% with the MLP. This indicates that the limiting factor is measurement information rather than neural architecture. Additional stratified checks show stable performance across topology states and fault-resistance bins, while CPU inference contributes only 0.528 ms to the one-cycle total decision time of 20.53 ms.
