# Fault Inception Detection in Real-World Disturbance Data for Power System Protection

arXiv: https://arxiv.org/abs/2606.23111 · HTML: https://mlsanity.com/paper/2606.23111/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2026.

## Built on by (most cited first, 2 of 2)

- [A Standardized Framework for Machine Learning in Power System Protection](https://mlsanity.com/paper/2608.20181/) (2026)
- [EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection](https://mlsanity.com/paper/2609.28149/) (2026)

## Abstract (arXiv)

Large collections of real-world disturbance recordings are increasingly available in transmission networks, but their value for power system protection and automated disturbance analysis is limited by the absence of precise event-onset annotations. In practice, field-recorded voltage and current waveforms contain switching operations, transformer energization, resonance, saturation, and other non-ideal effects that can obscure or mimic genuine fault signatures, making reliable fault inception detection difficult. This paper presents an training-free framework for fault inception detection in real-world transmission disturbance data. The method combines protection-domain indicators, robust median/MAD-based normalization, a low-latency transient path, and persistence-aware fusion and veto logic to distinguish fault-consistent disturbances from non-fault transients. We apply the framework to 12053 transmission-level recordings from the publicly available RTE database and further assess detector performance on a manually reviewed subset of 300 events. On the reviewed subset, the detector achieves 96.6% recall, 79.2% precision, and a median timing error of 4.2ms for matched detections. These results indicate that the proposed approach can support protection-oriented disturbance screening, relay and post-event analysis, and the creation of timestamp annotations for downstream data-driven monitoring tasks.