Non-Stationary Phase Digital Relay for Arcing Current Faults in Medium-to-Low Voltage Power Transformers
Bibliographic record
Abstract
Arcing current faults (ACFs) occurring in a medium-low voltage$3\phi$transformer represent a challenge for transformer protection. Such faults initiate currents with different features from those triggered by conventional faults, thus making it difficult for transformer protection to accurately detect and respond to arcing current faults. This paper proposes a method for accurate detection and identification of arcing current faults in MV-LV transformers. The presented method is based on extracting the magnitudes and phases of low frequency harmonics from the differential currents. Unlike magnetizing inrush and conventional fault currents, arcing current faults trigger currents that have harmonic components with non-stationary phases. These non-stationary phases can provide signature information of arcing current faults. Multi-channel filters can accurately extract harmonic components with complex time-frequency characteristics, including non-stationary phases. In this paper, a multi-channel filter bank that is used to extract harmonic components with non-stationary phases as a signature of ACFs on the low-voltage side of a medium-low voltage$3\phi$transformer. The used filter bank is composed of digital bandpass finite impulse response filters, each of which has a linear phase over a wide frequency range. The non-stationary phase method is tested during various fault and non-fault events. Test results demonstrate protection responses with speed, accuracy, and reliability against ACFs. Observed response features are found to have minor sensitivity to the level of loading level and/or type of the ACFs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".