Time Warping method for accelerated EMT simulations
Bibliographic record
Abstract
Electromagnetic transients (EMTs) are fast and decay within a few cycles. Since EMTs are fast, the simulation step size required to simulate an EMT is small, which may make the simulation of a power system with an EMT-type software computationally demanding. This paper proposes a hybrid simulation method called Time Warping (TW) method which makes use of the fast decaying nature of EMTs to accelerate EMT-type simulations. The idea is that small simulation step sizes are only required in the first few cycles following an EMT event where fast transients exist; when these EMTs have decayed and a steady-state is achieved, it is possible to warp to the next EMT event skipping the steady-state in between. The paper shows the effectiveness of the proposed time warping (TW) method by applying it to a simple test circuit and a practical network. The simulation results show that the proposed TW method is capable of accelerating EMT simulations while providing the same accuracy as a full EMT-type tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".