Real-Time MPSoC-Based Electrothermal Transient Simulation of Fault Tolerant MMC Topology
Bibliographic record
Abstract
Among different modular multilevel converter (MMC) submodule (SM) topologies, the clamp double submodule (CDSM) has the capability of dc fault current limiting and utilizes a relatively small number of switching devices. Since CDSM has a more complex circuit structure than half-bridge or full-bridge SM, it is a significant challenge for the real-time electromagnetic transient (EMT) simulation for a multiterminal dc (MTDC) system containing CDSM MMC. This paper proposes the device-level electrothermal model of CDSM for real-time EMT simulation, which can accurately present the power losses, the junction temperatures, and the switching transient waveforms of individual switches consuming more computation resources. The individual insulated-gate bipolar transistors of the CDSM MMC during fault clearance transient are evaluated from both electromagnetic and thermal perspectives, which interact with each other dynamically. To ensure the real-time performance of the proposed model, the equivalent circuit model is combined with the device-level model. The system-level and device-level waveforms during normal operation and dc fault transient for a three-terminal dc system are both presented and compared with PSCAD/EMTDC and SaberRD. The simulation system was implemented on the Xilinx Zynq UltraScale+ZCU102 multiprocessor system-on-chip (MPSoC) platform, and the results were captured by the oscilloscope in real-time.
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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) | 0.003 | 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".