The paper presents a machine‑learning framework for classifying power‑system contingencies into safe, moderate, or severe categories. Using Newton‑Raphson load flow data, the study applies SMOTE, PCA, and classifiers (KNN, Random Forest, SVM) to IEEE‑14 and IEEE‑30 bus systems, evaluating performance with precision, recall, and F1 score. Random Forest achieved the highest F1 scores, while PCA improved overall performance more than SMOTE, which boosted recall at the cost of some false positives.
By Joshua Salako, Folajimi Osikomaiya, Olakorede Olamiju
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.
By Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
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.
By Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea P\'erez-Toro, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
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.
By Sohini Roy, Xheni Hylviu
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.
By Julian Oelhaf, Georg Kordowich, Paula Andrea P\'erez-Toro, Christian Bergler, Johann J\"ager, Andreas Maier, Siming Bayer
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.
By Parikshit Pareek, Sidhant Misra, Deepjyoti Deka
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.
By Md Obaidur Rahman, Junjie Qin, Vassilis Kekatos
arXiv:2606. 27091v1 Announce Type: cross Abstract: LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data.
By Ryan Fetterman
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.
By Martin Sadric, Sebastian P\"utz, Christian Nauck, Veit Hagenmeyer, Frank Hellmann, Dirk Witthaut, Benjamin Sch\"afer
arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.
By Yi He, Zhengkang Guan, Anpeng Wu, Peng Cui, Fei Wu, Kun Kuang
arXiv:2509.05259v2 Announce Type: replace
Abstract: Automatic Generation Control (AGC) plays a critical role in maintaining power balance across multi-area power systems. However, its complete relian...
By Ahmad Mohammad Saber, Alok Paranjape, Jehad Jilan, Niranjana Naveen Nambiar, Amr Youssef, Deepa Kundur
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).
By Ana K. Rivera, Anvita Bhagavathula, Alvaro Carbonero, Priya Donti