Discrete time compensation of switching events for accurate real-time simulation of power systems
Bibliographic record
Abstract
This paper presents new simulation software that allows the efficient and accurate fixed-time-step simulation of complex event-based dynamic systems. The software, named ARTEMIS/sup TM/ (Advanced Real-Time Electro-Mechanical Transient Simulator), accurately simulates time-segment linear systems with discontinuities occurring anywhere between time-steps. The new software is used for the simulation of energy and power system dynamics, and is implemented in the ARTEMIS Add-On for Simulink's Power System Blockset. With ARTEMIS Discrete Time Compensation of Switching Events (DTCSE), the between-step events are transparently taken into account and compensated for in the simulation results. This eliminates sub-synchronous jitter while causing only minor increase in both the average time-step and real-time time-step (typically less than 1.0%) making ARTEMIS with DTCSE suitable software for real-time simulation of precision-critical hardware-in-the-loop (HIL) systems. The DTCSE algorithm is very good at handling typically challenging fixed-time-step simulation cases, like multiple single time-step discontinuities.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".