The growing penetration of renewable and distributed generation is transforming power systems and challenging conventional protection schemes that rely on fixed settings and local measurements. Machine learning (ML) offers a data-driven alternative for centralized fault classification (FC) and fault localization (FL), enabling faster and more adaptive decision-making. However, practical deployment critically depends on robustness. Protection algorithms must remain reliable even when confronted with missing, noisy, or degraded sensor data. This work introduces a unified framework for systematically evaluating the robustness of ML models in power system protection. High-fidelity EMT simulations are used to model realistic degradation scenarios, including sensor outages, reduced sampling rates, and transient communication losses. The framework provides a consistent methodology for benchmarking models, quantifying the impact of limited observability, and identifying critical measurement channels required for resilient operation. Results show that FC remains highly stable under most degradation types but drops by about 13% under single-phase loss, while FL is more sensitive overall, with voltage loss increasing localization error by over 150%. These findings offer actionable guidance for robustness-aware design of future ML-assisted protection systems.

翻译：可再生能源和分布式发电的日益普及正在改变电力系统，并对依赖固定设置和本地测量的传统保护方案构成挑战。机器学习（ML）为集中式故障分类（FC）和故障定位（FL）提供了一种数据驱动的替代方案，能够实现更快、更具适应性的决策。然而，实际部署的关键在于鲁棒性。即使面对缺失、噪声或退化的传感器数据，保护算法也必须保持可靠。本研究引入了一个统一框架，用于系统评估电力系统保护中机器学习模型的鲁棒性。通过高保真电磁暂态（EMT）仿真模拟现实中的退化场景，包括传感器中断、采样率降低和瞬态通信丢失。该框架提供了一致的方法论，用于基准测试模型、量化有限可观测性的影响，并识别弹性运行所需的关键测量通道。结果表明，故障分类在大多数退化类型下保持高度稳定，但在单相缺失情况下下降约13%；而故障定位整体更为敏感，电压缺失会使定位误差增加超过150%。这些发现为未来机器学习辅助保护系统的鲁棒性感知设计提供了可操作的指导。