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:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.
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:2605.23194v2 Announce Type: replace-cross Abstract: Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are ofte...
arXiv:2608. 04474v1 Announce Type: new Abstract: Data-driven decision pipelines combining predictive machine learning models with downstream optimization software are increasingly used to make high-stakes operational decisions.
arXiv:2510. 22048v4 Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces).
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.
The paper introduces a method that uses a single deep neural network, trained only on basecase AC power flow data, to predict post-contingency operating states for any single-line outage. It frames this prediction as a fixed-point iteration and provides sufficient convergence conditions, certifying them via semidefinite programming. Numerical experiments on the IEEE 118-bus system show that the certified conditions hold for all tested contingencies and that accurate state estimates are achieved in only a few iterations.
arXiv:2505. 05203v3 Announce Type: replace-cross Abstract: With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness.
arXiv:2607. 05187v1 Announce Type: new Abstract: As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of Bias Temperature Instability (BTI) and Process Variation (PV).
arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.
arXiv:2410. 04818v2 Announce Type: replace-cross Abstract: We present PINCO, an unsupervised learning framework that integrates Graph Neural Networks with physics-informed neural networks for AC optimal power flow (AC-OPF) solutions.
PowerZooJax is a JAX-based benchmark suite designed for reinforcement learning in power system operation. It offers five constrained Markov decision process tasks covering generation, transmission, distribution, distributed energy resources, and data center microgrids. By implementing power flow, economic dispatch, market clearing, and device dynamics as JAX computation graphs, the entire training and evaluation loop runs on the GPU, yielding significant speedups over CPU-based simulations and enabling standardized evaluation of policy returns, safety violations, and out-of-distribution stress conditions.