Heterogeneous Real-Time Co-Emulation for Communication-Enabled Global Control of AC/DC Grid Integrated with Renewable Energy
Bibliographic record
Abstract
The information and communication technologies (ICTs) are increasingly merging with the conventional power systems. For the design and development of modern AC/DC grids with integrated renewable energy sources, the system-level control schemes with ICTs involved should be evaluated in a co-simulation framework. In this work, a heterogeneous hardware real-time co-emulator composed of FPGAs, many-core GPU, and multi-core CPU devices is proposed to study the communication-enabled global control schemes of hybrid AC/DC networks. The electromagnetic transient (EMT) power system emulation is conducted on the Xilinx FPGA boards to provide nearly continuous instantaneous waveforms for cyber layer sampling; the communication layer is simulated on the ARM CPU cores of the embedded NVIDIA Jetson platform for flexible computing and programming; and the control functions for modular multi-level converters are executed on GPU cores of the Jetson platform for parallel calculation. The data exchange between FPGAs and Jetson is achieved via the PCI express interface, which simulates the sampling operation of the AC phasor measurement unit (PMU) and DC merging unit (DC-MU). The power overflow and DC fault cases are investigated to demonstrate the validity and effectiveness of the proposed co-emulation hardware architecture and global control schemes.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".