Design and Implementation of Real-Time Mpsoc-FPGA-Based Electromagnetic Transient Emulator of CIGRÉ DC Grid for HIL Application
Bibliographic record
Abstract
Real-time electromagnetic transient simulation is a powerful tool for the power system transient study and the hardware-in-the-loop (HIL) testing. Large-scale DC grid can meet the flexible transmission requirements with high power efficiency and high controllability. CIGRÉ working group has proposed a DC grid test system, which covers various HVDC configurations and deploys modular multi-level converters (MMCs) in the grid. This work focuses on the efficient solution of the DC grid real-time emulator providing accurate and detailed results. The design and implementation of the CIGRÉ DC grid are carried out on a hybrid MPSoC-FPGA platform realizing the synergy between the Xilinx Vitrex UltraScale+ FPGA device containing a large number of logic resources and Xilinx Zynq UltraScale+ MPSoC device containing the ARM multi-core processing system and FPGA resources on a single chip. Hybrid modeling methodology using device-level electrothermal model, equivalent circuit model, and average value model for the converters is employed to present the detailed device-level results of local equipment and the accurate system-level results of global interactions of the DC grid. The detailed design partitioning and implementation methods are presented, and the real-time results are captured by the oscilloscope and validated with commercial simulation tools PSCAD/EMTDC and SaberRD.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".