Increasing Line Outage Localization Performance with Ensemble Classifiers

Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure correlations from cascade data, but they are trained and evaluated on a single grid, and transferring the learned knowledge to an unseen grid remains an open problem. We address this by training a single Gated Recurrent Unit (GRU)-gated Graph Attention Network on combined cascading failure data from limited training grids and applying it directly to any unseen grid without retraining. A GRU gate controls what information each node retains or discards at each cascade iteration. Empirical evaluation shows that the model transfers zero-shot to multiple new grids spanning inter-time and inter-domain settings. Using information extracted from the trained model, we consistently identify more vulnerable lines than established structural and electrical baselines.

The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional relay protection systems, which operate based on static rules and predefined thresholds, are inadequate for addressing these challenges, particularly in detecting and isolating faults such as short circuits. Consequently, the conventional methodologies applied to electrical network protection frequently fail to achieve optimal performance in fault detection, especially in terms of adherence to safety standards and the selective limitation of damage. Recent research indicates that machine learning (ML)-based approaches can effectively tackle these issues; however, variations in grid configurations and analysis windows have impeded consistent comparative assessments. In this study, we assess the efficacy of various ML models in detecting electrical faults and pinpointing defective transmission lines within a 10 ms measurement interval - a critical time-frame for real-time operational viability, for the first time. The most effective model attained an F1 score of 0.991 +/- 0.018 and demonstrated a processing time of 0.342ms +/- 0.509ms.

Utilities publish Estimated Times of Restoration (ETRs) for customer-facing storm outages, and their accuracy governs whether customers can make sound decisions about food, medical equipment, and relocation. Prior work treats ETR as static tabular regression in which each outage contributes one record, discarding the fact that every development of an outage, from crew assignment through dispatch, suspension, damage assessment and partial restoration, is recorded as a revision. We reformulate ETR prediction as longitudinal tabular regression and introduce a Longitudinal Tabular Transformer (LTT), an axial-attention model that consumes the revisions preceding a prediction and issues a refined estimate at every one. On 242{,}928 storm-attributed outages from a cohort of 526{,}468 filtered events and 10.0 million revisions at six operating companies, LTT reduces customer-weighted asymmetric error at all six, by a median of 36.9,% against the estimates the utilities published during the same storms and 11.3,% against the strongest learned baseline at each. It is the only method improving on the incumbent's satisfaction impact at all six companies while also reducing root mean squared error at all six. Stratification by revision index shows that LTT error is largest at the first revision, where no history is available, and falls monotonically as revisions accumulate.