Bibliographic record
Abstract
This paper introduces a frequency-domain approach for investigation of the phenomenon of torsional dynamics. The Nyquist criterion is adopted by the proposed method to identify stability regions with respect to torsional oscillations. This paper also introduces two performance indices to evaluate torsional dampings and propensity of the system to experience torsional oscillations. The proposed method is an alternative approach to the eigenvalue analysis method and the complex torque method for investigation of torsional dynamics. The salient features of the method as compared with the aforementioned two methods are: 1) its computational efficiency since it utilizes the open-loop transfer-function matrix of the system and inherently more advantages for large size systems; 2) its performance indices that readily reveal "damping of" and "propensity to" a torsional oscillatory mode; and 3) its capability to formulate a multimachine system which includes both induction and synchronous machines. The proposed method is applied to the First IEEE Benchmark Systems for SSR studies and the results are verified based on comparison with those obtained from eigenvalue studies and digital-computer time-domain simulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".