arXiv:2606. 24298v1 Announce Type: cross Abstract: The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform datasets that enable transparent and reproducible evaluation.
arXiv:2608. 20181v1 Announce Type: cross Abstract: Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting.
The paper introduces Distributionally Robust Conformal Safety Screening (DR‑CSS), a policy‑agnostic framework that uses historical data and a nominal simulator to pre‑deploy safety checks for new voltage control policies in active distribution grids. DR‑CSS constructs conformal safety intervals around simulated voltage trajectories, enlarging them to account for closed‑loop effects of the new policy. Experiments on IEEE 33‑bus and 141‑bus systems show that DR‑CSS successfully identifies all unsafe scenarios while adapting intervals to reduce false warnings.
EvEMTBench is an open, executable, and versioned benchmark designed to standardize the evaluation of machine‑learning methods for power system protection. It defines 12 protection and event‑analysis functions across four grids (20–345 kV) as 24 scored tasks, enabling structured assessment under varied observability, distribution shifts, and cross‑grid transfer scenarios. The benchmark includes committed data partitions, leakage controls, and reproducible reporting, and demonstrates that wider observability does not always help, that shifted conditions expose hidden failures, and that fault detection transfers better than fault localization.
arXiv:2510. 00831v2 Announce Type: replace Abstract: The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks.
arXiv:2609.16744v1 Announce Type: new Abstract: The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery...
arXiv:2308. 07867v4 Announce Type: replace-cross Abstract: The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy.
arXiv:2607. 16031v1 Announce Type: cross Abstract: Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning.
arXiv:2607. 27681v1 Announce Type: new Abstract: Transient-stability assessment determines whether a power system can recover after a disturbance and is therefore essential to preventing generator trips and cascading outages.
arXiv:2606. 15856v1 Announce Type: cross Abstract: Operational disturbance monitoring in power networks requires decisions to be made from waveform windows as they arrive, rather than from completed records after the event.
arXiv:2607. 13221v1 Announce Type: cross Abstract: Real-time N-1 contingency screening in an energy management system trades assurance against cost: verifying every credible outage with full power flow is too slow, while fast linear-sensitivity screening gives no statistical guarantee and can silently pass unsafe operating points, especially when a controller drives the system into unfamiliar regimes.
arXiv:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.
arXiv:2607. 08918v1 Announce Type: new Abstract: Cyber-physical power systems are vulnerable to cascading failures caused by tight interdependencies between power and communication infrastructures.