Identification of voltage collapse margins in power systems
Bibliographic record
Abstract
Voltage collapse has been associated with a stress condition of power systems. Control actions must provide the desired results, otherwise a system may operate in an unknown condition. It has been shown that this unknown condition is associated with two regions of operation and the boundary between them. The boundary between the two regions is associated with a singular load-flow Jacobian. In order to identify the critical bus, a reduction of the load-flow Jacobian in relation to each load bus is derived. To reduce computational burden associated with large power networks, a network partitioning is proposed based on voltage variation at each load bus in relation to load variation at the other load buses. For calculating the margins, for all load buses of the weak area of the power system identified from network partitioning, the relation between load variation at each load bus and voltage magnitude and angle phase variations at the critical bus are normalized, one by one. The buses strongly connected to the critical bus have smaller load variation in relation to the buses weakly connected to the critical bus. The proposal method has been tested using an IEEE 24-bus system.>
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".