A Switching-Function-Based Average Value Model for Efficient EMT-type Simulation of Solid-State Transformer with Interleaved DAB Modules
Bibliographic record
Abstract
Conventional electromagnetic transient (EMT) simulation of solid-state transformer (SST) requires tremendous computational efforts due to the detailed representation of discrete circuit components of the SST. This paper presents a numerically accurate and efficient average value modeling strategy of the SST. Switching-function-based average value model (SFB-AVM) is proposed to simplify the SST equivalent circuit and to accelerate the EMT simulation. Switching interleaving operation of the dualactive bridge (DAB) modules of the SST is accurately modeled in the proposed SFB-AVM. Circuit decoupling and constant network conductance (G)-matrices in the nodal voltage equation are achieved in the proposed SFB-AVM. Furthermore, switching interpolation technique is proposed to capture semiconductor switching events accurately in the proposed SFB-AVM. Significantly improved simulation efficiency of the proposed SFBAVM is demonstrated in the case studies by comparing to the detailed model (DM) and detailed equivalent model (DEM).
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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 | 0.000 |
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".