Real-Time FPGA-RTDS Co-Simulator for Power Systems
Bibliographic record
Abstract
This paper proposes a co-simulation platform using field-programmable gate array (FPGA) and real-time digital simulator (RTDS) for the simulation of large power systems. It combines the advantages of high computational power from FPGA and better modelling flexibility from RTDS together. The FPGA therefore acts as an efficient and economical extension to RTDS especially when simulating large ac systems. One of the significant advantages of the proposed co-simulator is that it avoids the potential interface error existing in the conventional approach of interfacing transient stability program with electromagnetic transient programs. Two key aspects of the proposed co-simulator are discussed: 1) the interface design between FPGA and RTDS and 2) the hardware implementation and expandability of the platform. Two case studies are presented to verify the simulation accuracy and capability of the proposed co-simulator. The first case simulates a two-area four-machine power system with one area simulated in FPGA and the other area in RTDS. Comparisons are made with the case where the complete system is simulated in RTDS. The second case simulates a system of 141 buses in FPGA to demonstrate the simulator’s capability in simulating large power systems.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".