Power grid correction using sensitivity analysis under an RC model
Bibliographic record
Abstract
Verifying an RC model of the power grid requires one to check if the steady state voltage drops on all the nodes of the grid do not exceed a certain threshold. We propose an approach to correct the grid, in case some voltage drops violate the threshold condition, by making minor changes to the original design. Previous work has been done in [1] on the DC model of the grid and this paper deals with the transient model. Rather than directly reducing the steady state voltage drops below the threshold we work on reducing the first time step voltage drops. The method uses current constraints proposed in [2] to find the first time step voltage drop whose distance to the corresponding threshold is the largest. It then tries to estimate it as a function of the metal widths on the grid. A non-linear optimization problem is then formulated and the required metal line width changes that reduce the first time step voltage drops by a sufficient amount are then determined. The reduction of the first time step voltage drop by that amount will make the steady state voltage drops of all the nodes less than the threshold.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".