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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.
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: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 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.
The paper introduces a hybrid two‑stage machine learning pipeline for fault detection and classification in high‑voltage transmission networks. Stage 1 uses an Isolation Forest anomaly detector combined with an optional supervised binary detector, while Stage 2 applies a Random Forest multiclass classifier only to samples flagged by Stage 1. Feature engineering maps six raw channels to eighteen features, including zero‑sequence symmetrical components, achieving end‑to‑end accuracies of 95.8 % on the TLFaultDataset and 97.25 % on an independent single‑point dataset, surpassing federated benchmarks without GPU or federated infrastructure.
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.