High-Speed Electromagnetic Transient (EMT) Equivalent Modelling of Power Electronic Transformers
Bibliographic record
Abstract
The high-speed accurate electromagnetic transient (EMT) simulation of the power electronic transformers (PET) has become a challenge, due to the microsecond-range time steps and the large number of high-frequency semiconductor switches and isolating transformers. Taking the input-series-output-parallel (ISOP) connected cascaded H-bridge (CHB) type dual active bridge (DAB) based PET as an example, this paper proposes a general high-speed equivalent EMT modelling method of PET. First, the isolating transformer within each DAB is discretized into two-port Norton circuits. Second, each CHB-DAB is equivalent to two single-port circuits by eliminating the internal nodes. And the input-side is represented by a Thévenin circuit and the output-side is represented by a Norton circuit. Third, all the CHB-DABs are equivalent to a two-port circuit with all the internal node information preserved. Fourth, the implementation of PET blocking is also considered for startup and fault protective actions. Fifth, the stability of the proposed discrete decoupling method of the PET is verified that it does not introduce specific limitation on the simulation step size. Finally, the developed PET models on PSCAD/EMTDC are shown to be two orders of magnitude faster than the currently available fully-detailed models with negligible loss of accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".