Faster-Than-Real-Time Hardware Emulation of Transients and Dynamics of a Grid of Microgrids
Bibliographic record
Abstract
Enhanced environmental standards are leading to an increasing proportion of microgrids (MGs) being integrated with renewable energy resources in modern power systems, which brings new challenges to simulate such a complex system. In this work, comprehensive modeling of a grid of microgrids for faster-than-real-time (FTRT) emulation is proposed, which can be utilized in the energy control center for contingencies analysis and dynamic security assessment. Electromagnetic transient (EMT) modeling is applied to the microgrid in order to reflect the detailed device processes of the converter and renewable energy sources, while the AC grid utilizes the transient stability modeling to reduce the computational burden and obtain a high acceleration value over real-time execution. Consequently, a dynamic power injection interface is proposed for the coexistence of the two simulation types. The reconfigurability and parallelism of the field-programmable gate arrays (FPGAs) enable the whole system to be executed in FTRT mode with 51 times acceleration over real-time. Meanwhile, three case studies are emulated and the results are validated by the off-line simulation tool Matlab/Simulink®.
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) | 0.003 | 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".