A Research on Selection of Appropriate Stability Index under Adverse System Conditions for the Assessment of Voltage Stability of an IEEE 14 Bus Power System
Bibliographic record
Abstract
For voltage stability assessment at a given operating point, various types of voltage stability indices (VSIs) have been proposed in the literature. In this paper, the voltage stability assessment of an IEEE-14 bus system is done for performance comparison of different types of VSIs available, under certain critical and practical stressed operating conditions (SOCs). The performance comparison of various VSIs under the considered SOCs is not reported in the literature. Such SOCs include the combinational occurrence of – variation in inductive loading, single line to ground (SLG) fault and effect of one generation unit tripped. These SOCs are the prime cause of voltage collapse of any node/line. The results show the performance of various VSIs with respect to line number, contingency ranking of the line, power margin, effects of loading and SLG fault. These VSIs are also instrumental in critical line and node analysis (CLNA) which is useful in the choice of proper location for reactive power compensation required. The simulated results provide the best performing VSI for accurate prediction of voltage instability under any considered SOC. This information is essential for voltage stability assessment of a particular line under multiple causes of voltage collapse.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 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".