dq0 PHT-Based Digital Differential Protection for 3φ Converter Transformers
Bibliographic record
Abstract
This paper presents a new digital differential protection for three phase (3φ) converter transformers. The proposed digital differential protection is developed to comply with the Standard ANSI 87 T, and is designed to detect and identify internal faults based on the energy contents of the high frequency subbands present in the d - q-axis components of the differential currents. Desired energy contents are quantified using the phaselet transform (PHT), which can process signals without sensitivity to the variations in their phase shifts. Energy contents of the high frequency sub-bands offer accurate, fast, and reliable detection and identification of internal faults in any part of a 3φ converter transformer. The d - q PHT-based digital differential protection is implemented for performance evaluation using different 3φ converter transformers, when feeding controlled rectifier units. Performance results demonstrate accurate, fast and reliable detection and response to different types of fault and non-fault events. Response features of the developed differential protection are complimented with simple implementation, reduced computations, and minor sensitivity to phase shifts.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".