Real-Time Device-Level Simulation of MMC-Based MVDC Traction Power System on MPSoC
Bibliographic record
Abstract
Real-time simulation of device-level power electronic converter models plays an essential role in traction power systems by allowing accurate prediction of device stresses to design improved control and protection schemes. This paper proposes the electrothermal behavioral power electronic models for the modular multilevel converter (MMC)-based medium voltage direct current (MVDC) traction power system based on the Wiener-Hammerstein configuration. The new configuration introduces the carrier charge prerequisite dynamic transients before device turn-ON or turn-OFF operation. The equivalent carrier charge circuit is also proposed, and the first-order delay assumption of turn-ON and turn-OFF delay time has been proven by the device datasheet. The power electronic device models are implemented in a Xilinx® Zynq® multiprocessing system-on-chip platform. By utilizing hardware and software codesign, both 25-μs time-step system-level and 100-ns time-step device-level transients can be captured in real time within a single device. The three-phase unbalance issue has been resolved by introducing the three-phase to single-phase MMC topology. In the case study, the MMC-based MVDC traction power system has been utilized for the performance of the proposed electrothermal behavioral power electronic models by the off-line simulation models on SaberRD® for device-level transients and PSCAD/EMTDC® for system-level transients.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".