EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection

2026-09-23

AI Summary

为了解决电力系统保护中机器学习研究难以比较的问题，EvEMTBench提供了一个开放、可执行且版本化的基准，通过定义12个保护和事件分析功能来标准化评估条件。

Abstract

Studies of machine-learning-based power system protection are difficult to compare because task definitions, measurement access, data partitions, metrics, and generalization conditions often differ. EvEMTBench addresses this gap with an open, executable, and versioned benchmark that fixes these evaluation choices while leaving model design open. Across four grids spanning 20-345 kV, it defines 12 protection and event-analysis functions instantiated as 24 scored tasks and supports structured evaluation across observability conditions, predefined distribution shifts, and zero-shot and fine-tuned cross-grid transfer. Committed partitions, leakage controls, and reproducible reporting provide a common basis for comparing future methods. A reference evaluation spanning trivial, conventional, feature-based, and deep-learning baselines shows that wider observability is not uniformly beneficial, shifted conditions can reveal failures not apparent in-distribution, and cross-grid transfer is substantially stronger for fault detection than for fault localization. Protection-relevant diagnostics identify failure modes not apparent from primary metrics alone. EvEMTBench therefore makes generalization in machine-learning-based protection an explicit and reproducible evaluation problem.

Problem

• machine learning
• power system protection
• benchmark
• evaluation
• generalization

Innovation

• open benchmark
• power system protection
• structured evaluation
• cross-grid transfer
• generalization