Design and Optimization of Air-Core HTS Pulse Transformer for Series-Type Hybrid Circuit Breaker (S-HCB)
Bibliographic record
Abstract
This paper discusses the design and optimization of a unique HTS air-core pulse transformer for the new class of dc series type hybrid circuit breakers (S-HCBs) operating in the liquid nitrogen (LN2) under cryogenic environment. The HTS secondary windings of the new pulse transformer carry a dc load current offering ultralow power loss during the normal operation unlike the conventional solid state circuit breakers (SSCBs). Unlike the prior-art HTS power transformers and the superconducting fault current limiters (SFCLs), the new pulse HTS transformer is uniquely designed to prevent the HTS tapes from quenching despite the set of constraints imposed by the S-HCB operation including a large pulse current up to 6kA and high mechanical stress. A new transformer optimization methodology is proposed using the genetic algorithm (GA) in MATLAB coupled with SIMULINK and a finite element model (FEM) in COMSOL to find the optimal transformer design meeting the overall 10kV/150A S-HCB design requirements within the electromagnetic constraints of the HTS. A 10kV/150A HTS transformer prototype is built and the electrical parameters are measured and found consistent with the simulated parameters. The S-HCB with the HTS transformer is tested at 600V/30A and 5kV/150A to validate the design. The experimental and COMSOL simulation results showed the successful operation of the HTS transformer and the fault currents of 30A and 150A were forced down to zero within$10\mu s$and remain as a small ripple current for$200\mu s$before a series mechanical switch opens arclessly and with no damage caused to the HTS windings.
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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 | 0.001 | 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".