Partial Refactorization Techniques for Electromagnetic Transient Simulations
Bibliographic record
Abstract
This paper explores partial refactorization techniques to accelerate the simulation of Electromagnetic Transients (EMTs) in power systems. Direct sparse left-looking LU factorization from the KLU solver is used to solve network equations. The refactorization step can be time-consuming if the factorized matrix varies often as the simulation involves power electronics switching or nonlinear devices. A path-based partial refactorization technique is proposed to accelerate the re-computation of LU factors. In the left-looking algorithm, only a subset of columns that belong to the computed factorization path are refactorized. In addition, Block Triangular Factorization (BTF) is enhanced through partial refactorization, which further accelerates computation through smaller, evolving submatrices. The new techniques are tested on large power grids. Substantial performance gains are achieved.
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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) | 0.000 | 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".