Simulation of modular multilevel converter and DC grids on FPGA with sub-microsecond time-step
Bibliographic record
Abstract
Real-time simulation is an important tool of validating the performance of the modular multilevel converter (MMC). By using the field programmable gates array (FPGA), MMC valves with hundreds of sub-modules and the large number of inputs and outputs can be simulated in real-time. In the traditional FPGA based real-time simulation, only the MMC valves are implemented on FPGA, while the rest parts including AC or DC grids and inductors are implemented on CPU. However, the subsystem simulated on CPU has slower dynamics than the subsystem simulated on FPGA. There is a delay of CPU time-step between these two subsystems, which may affect the accuracy of the results especially under fault condition. This paper presents a real-time simulation of modular multilevel converters, and DC grids fully based FPGA with sub-microseconds time-step. Benefitting from using the electric hardware solver (eHS), all the components including the MMC valves and DC grid system can be easily implemented on FPGA. Thus, the high-fidelity of the simulation is guaranteed. The results of the proposed FPGA based real-time simulation are provided.
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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) | 0.005 | 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".