EvEMTBench：电力系统保护中机器学习的开放基准
EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection
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中文总结 AI 辅助
EvEMTBench提出一个开放、可执行的基准，统一电力系统保护中机器学习任务的评估条件，通过多电网和迁移测试揭示泛化挑战。
中文摘要 AI 辅助
基于机器学习的电力系统保护研究难以比较，因为任务定义、测量访问、数据划分、指标和泛化条件往往不同。EvEMTBench通过一个开放、可执行且版本化的基准来解决这一差距，该基准固定了这些评估选择，同时保留模型设计的开放性。在跨越20-345 kV的四个电网中，它定义了12个保护和事件分析功能，实例化为24个评分任务，并支持在可观测性条件、预定义分布偏移以及零样本和微调跨电网迁移下的结构化评估。固定的数据划分、泄漏控制和可复现的报告为比较未来方法提供了共同基础。一项涵盖平凡、传统、基于特征和深度学习基线的参考评估表明，更广泛的可观测性并非普遍有益，偏移条件可能揭示分布内不明显失效，跨电网迁移在故障检测方面明显强于故障定位。保护相关诊断识别出仅凭主要指标无法显现的失效模式。因此，EvEMTBench使基于机器学习的保护中的泛化成为一个明确且可复现的评估问题。
英文摘要
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.
发表机构
- Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
- Ostbayerische Technische Hochschule Amberg-Weiden（东巴伐利亚安贝格-魏登应用技术大学）
机构由 AI 辅助整理，请以论文原文为准。
