标题： 用于电力系统保护中故障分类和定位的机器学习模型基准测试 显示英文标题

标题： Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection

摘要： 随着分布式能源资源（DERs），特别是可再生能源的日益融合，给电力系统保护带来了重大挑战，其中故障分类（FC）和故障定位（FL）是最关键的任务之一。 传统的保护方案基于固定阈值，在电网复杂性增加和动态条件下无法可靠地识别和定位短路。 机器学习（ML）提供了一个有前景的替代方案；然而，跨模型和设置的系统基准仍然有限。 本工作首次基于EMT数据，对电力系统保护中的FC和FL的经典ML模型进行了比较基准研究。 使用将电压和电流波形分割为10毫秒至50毫秒滑动窗口的数据，我们在现实的实时约束下评估了这些模型。 性能从准确性、对窗口大小的鲁棒性以及运行时效率方面进行评估。 表现最佳的FC模型获得了0.992$\pm$0.001的F1分数，而顶级FL模型的R2达到了0.806$\pm$0.008，平均处理时间为0.563毫秒。 显示英文摘要

摘要： The increasing integration of distributed energy resources (DERs), particularly renewables, poses significant challenges for power system protection, with fault classification (FC) and fault localization (FL) being among the most critical tasks. Conventional protection schemes, based on fixed thresholds, cannot reliably identify and localize short circuits with the increasing complexity of the grid under dynamic conditions. Machine learning (ML) offers a promising alternative; however, systematic benchmarks across models and settings remain limited. This work presents, for the first time, a comparative benchmarking study of classical ML models for FC and FL in power system protection based on EMT data. Using voltage and current waveforms segmented into sliding windows of 10 ms to 50 ms, we evaluate models under realistic real-time constraints. Performance is assessed in terms of accuracy, robustness to window size, and runtime efficiency. The best-performing FC model achieved an F1 score of 0.992$\pm$0.001, while the top FL model reached an R2 of 0.806$\pm$0.008 with a mean processing time of 0.563 ms.