arXiv:2607. 17008v1 Announce Type: cross Abstract: In many cases, the outage of one transmission line in a system can be localized by monitoring the power flow of another line, and machine learning methods can be used to distinguish the cases under uncertainty.
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.
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.
This study evaluates large language models (LLMs) for predicting weather‑related forced outage risk in a distribution grid using a zero‑shot approach without labeled training data. The task is framed as binary severity classification over 3h, 6h, and 12h horizons, leveraging six years of outage records and high‑resolution weather data from central Texas. Four zero‑shot LLMs are compared to two supervised classifiers under two input settings—current weather observations and forecast data—showing that supervised models lead on macro‑F1 and precision, while newer LLMs achieve competitive scores and offer complementary strengths in reasoning and geographic scalability.
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 learned control variates (CVs) to estimate rare grid‑outage probabilities at electric vehicle charging stations more efficiently. By training a classifier on inexpensive features such as temperature and load forecasts, the CV reduces variance in Monte Carlo simulations, with performance tied to the classifier’s recall and false‑positive rate. Experiments on a feeder‑overload simulator and a real Surabaya network show that even a weak learner can cut variance by a quarter, while a boosted‑tree learner achieves about 60% reduction, leading to a one‑third decrease in city‑wide blackout days compared to standard Monte Carlo.
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: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 study examines how machine‑learning fault detectors trained on data from specific relay locations perform when applied to other relays in a 110 kV transmission network. Using 50 ms windows and a multilayer perceptron, the authors evaluate all 64 source–target relay combinations and find that overall F1‑scores remain similar, but the false‑positive rate rises markedly while the false‑negative rate falls when models are transferred across relays. Analysis shows that models retain coarse bus‑group information, and that aggregate metrics can mask location‑specific changes in false‑alarm behavior, underscoring the need to assess error types separately for cross‑relay generalization.
The paper introduces a data‑onboarding pipeline that detects and repairs defects in building meter data before training machine‑learning demand‑forecasting models. Controlled experiments on hourly electricity data from twelve U.S. buildings show that the pipeline restores forecast accuracy to clean‑data levels, even when defects affect up to 1.6% of training rows, and works across multiple models and horizons. The study also documents a first‑version over‑cleaning issue, its cause, and a per‑building calibration that corrects it, with all results reproducible from publicly pinned inputs.
arXiv:2608. 11083v1 Announce Type: cross Abstract: Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning.
arXiv:2606. 00161v1 Announce Type: cross Abstract: The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods.
arXiv:2608.24665v1 Announce Type: new Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whe...