Phaselet-Based Arc Flash Relay Against Low Voltage Side Arcing Current Faults in MV-LV Power Transformers
Bibliographic record
Abstract
Arcing current faults (ACFs) are undesired transient events that can occur in different power system equipment, including medium voltage-to-low voltage (MV-LV) power transformers. The main challenge in detecting, identifying, and responding to low-voltage (LV) side ACFs (in a MV-LV power transformer), is due to low magnitudes of MV side currents triggered by a LV side ACF. As a result, MV side protective devices fail to detect and respond to LV side ACFs. In many cases, the reduced ability to detect and respond to LV side ACFs prolongs the duration of LV side ACFs, and leads to a significant increase in the incident energy (may exceed acceptable limits). In this article, an analysis of MV side currents is developed to extract signature information to ensure accurate and fast detection and identification of LV side ACFs. The proposed signature of a LV side ACF is the high frequency components (with non-stationary phases) extracted from MV side currents. Desired frequency components can be extracted using a multi-channel filter bank that is composed of digital high pass filters with linear phase responses. Such digital filters are designed using phaselet functions to ensure a simplified implementation of the desired filter bank. The accuracy and response speed of the proposed approach are utilized for designing a new arc flash relay (AFR) for MV-LV power transformers. The phaselet-based AFR is implemented and tested for a 35 kVA transformer during several transient events including LV side ACFs. Performance results reveal accurate and reliable detection, identification, and response to LV side ACFs with negligible sensitivity to loading level and/or ACF type (series or parallel).
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".