Digital Differential Protection for $3\phi$ Solid-State Transformers
Bibliographic record
Abstract
This article presents the development, implementation, and testing of a digital differential protection for three-phase (3φ) solid-state transformers (SSTs). The developed digital differential protection is designed to implement the ANSI 87 T protection function. The differential currents used by the developed protection are created by the dq0 components of the currents flowing in the high and low voltage sides of a 3φ SST. Internal faults are detected and identified using the energy contents of high-frequency subbands present in the dq0 differential currents. The desired high-frequency subbands are extracted using the phaselet transform that processes signals without sensitivity to nonstationary changes in their frequency and/or phases. Energy contents in the extracted high-frequency subbands allow accurate, fast, and reliable detection and identification of faults in any part of a 3φ SST. The proposed digital differential protection is implemented for performance testing using a laboratory 25-kVA 3φ SST. Performance results demonstrate accurate, fast, and reliable response to different fault and nonfault events. Furthermore, responses of the developed differential protection are found to be consistent regardless of the fault type, fault location, and/or loading level.
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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 | 0.001 | 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".