The Reconfigurable-Hardware Real-Time and Faster-Than-Real-Time Simulator for the Analysis of Electromagnetic Transients in Power Systems
Bibliographic record
Abstract
The reconfigurable-hardware real-time power system simulator (RH-RTS) is a field-programmable gate-array (FPGA)-based real-time simulator that is developed based on the concept of simulators hardware reconfigurability (i.e., to change the underlying hardware architecture of the simulator to accommodate various power system topologies). The uniqueness of the RH-RTS is the underlying hardware architecture. The RH-RTS has a massively parallel customized hardware architecture that is tailored to the solution of the mathematical model of the power system under consideration. The RH-RTS enables the simulation of large power systems with a computation-time per simulation time-step in the range of tens of nanoseconds. Not only does the RH-RTS provide a means for real-time operation (e.g., for closed-loop testing of physical control/protection platforms in hardware-in-the-loop (HIL) configuration), it also provides a means for faster-than-real-time operation (e.g., for statistical switching studies). This paper provides validation and evaluation of the performance of the RH-RTS. This paper presents a case study involving the simulation of a power system in both real time and faster than real time. The computation time per simulation time-step is as low as 24 ns, for realistic size systems, which is, by far, the lowest computation time reported in the technical literature.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".