Hybrid Simulation and Off-the-Shelf Hardware for Efficient Real-Time Simulation Studies
Bibliographic record
Abstract
A real-time simulation study, where a control system replica is connected to a real-time electromagnetic transient simulator in closed-loop (known as hardware-in-the-loop), is an essential tool in commissioning or refurbishment projects of major power system devices such as static var compensators, high-voltage direct-current transmission systems and other flexible alternative-current transmission systems: it allows to reduce commissioning risks and it is useful throughout the life of the system. It is however an expensive endeavor in time, manpower and equipment. The present paper aims at presenting a more affordable and efficient way to pursue realtime hardware-in-the-loop commissioning studies by using 1) hybrid simulation (electromagnetic transient and transient stability modeling) to increase the scale of the power system that can be simulated in real-time without increasing the required real-time hardware resources and 2) low-cost commercially off-the-shelf computers. To illustrate this approach, a hardware-inthe-loop experimental setup is presented and detailed: based around the control system replica of Hydro-Québec's Figuery static var compensator, this setup shows how hybrid simulation and low-cost personal computers can be used to reduce the cost of real-time simulation studies.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".