Top-Down/Bottom-Up Method for Identifying a Set of Voltage Stability Preventive Controls
Bibliographic record
Abstract
In this work, a novel method for choosing a global set of preventive controls for voltage stability analysis is developed. The method depends on top-down and bottom-up approaches to obtain the set of controls according to voltage stability sensitivity analysis. At first, the sensitivities of the voltage stability margins of network buses to preventive controls are obtained. The average of the sensitivities referred to each preventive control is then calculated, which gives an indicator about how each preventive control can improve the voltage stability margin of power system. The average is done over the sensitivities of different critical buses located at different locations. The two approaches (top-down and bottom-up) are then performed on the averaged sensitivities to choose a set of the most efficient controls. The proposed method is able to simultaneously eliminate the impact of system contingencies on network buses. The proposed method is checked on the IEEE 39 bus network. The results demonstrate the accuracy and the validity of the proposed method for control selection.
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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 | 0.001 | 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".