← Back to all news
arXiv AI By Joshua Salako, Folajimi Osikomaiya, Olakorede Olamiju

Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Sep 24

EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection

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
fine-tuningbenchmarks
More like this →
arXiv AI
Jun 19

Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection

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
More like this →