Increasing Line Outage Localization Performance with Ensemble Classifiers
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. In this study, we examine the improvements in line outage localization performance achieved by various ensemble classifiers compared to single-model methods. In the case studies, we compared the classification results with measurement data collected at observed transmission lines (OTLs) selected using three algorithms, i.e, greedy maximum coverage problem (MCP), high-eta, and random selection, based on two sensitivity factors, i.e., line outage distribution factors (LODFs) and line...
| Authors | Daniel Flores, Yuanrui Sang, Michael P. McGarry |
|---|---|
| Primary category | eess.SY |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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