Analysis of ill-conditioned power-flow problems using voltage stability methodology
Bibliographic record
Abstract
Ill-conditioned power-flow problems have been widely investigated and reported in the literature. A typical approach develops enhanced solution algorithms when a power-flow case is found divergent with the conventional Newton method. It is known that a genuine ill-conditioned problem is caused by the presence of a large condition number in the power-flow Jacobian matrix. Since a large condition number is associated with small singular values or eigenvalues of a matrix and the voltage collapse is also related to small eigenvalues, it is therefore postulated that an ill-conditioned power-flow problem is actually a voltage collapse problem. The objective of this paper is to investigate the relationship between power-flow ill-conditioning and voltage instability. The findings confirm that power-flow ill-conditioning only occurs at the voltage collapse point. As a result, developing improved algorithms to solve the problem is an unprofitable strategy. The well-known voltage stability assessment techniques such as the PV curve method are sufficient for the problem. This conclusion is supported with case studies on five widely known ill-conditioned power-flow problems and rigorous mathematical analysis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".