Flexible, Scalable FPGA-based Real-Time Simulation Model for Solid-State Transformer Studies
Bibliographic record
Abstract
Solid-state transformers (SST) merit attention due to their flexibility and high efficiency. They are now being used in medium voltage distribution systems for renewable integration, electric vehicle charging, and in data centers. SSTs have a multi-phase, multi-stage, and multi-port structure combining AC-DC converters and DC-DC converters to handle both AC and DC inputs and outputs. Hardware-in-the-loop (HIL) testing with real-time simulation is essential for validating the control and protection systems of the SST. Simulating the SST in real-time is challenging due to the large number of switches present as well as their high switching frequency. To this end, an FPGA-based high-fidelity real-time model for accurate simulation of SSTs is presented in this paper. The developed FPGA model is both flexible and scalable to simulate different SST topologies with a combination of H-bridges, and dual active bridges (DAB) in real-time. These units can be connected in different ways (cascaded, series, parallel) to fit diverse converter topologies. Up to 64 DABs can be simulated in a single FPGA with simulation timesteps as low as 40 ns. Real-time simulation results are presented to validate the development of this FPGA-based model using a representative AC-DC SST topology.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".