On the Application of the Complex Torque Coefficients Method to the Analysis of Torsional Dynamics
Bibliographic record
Abstract
The complex torque coefficients method has been widely accepted for the analysis of the phenomenon of torsional interaction of turbine-generator units in power systems. This paper shows that, depending on the system parameters and the operating point, the complex torque coefficients method may exhibit limitations and not accurately and fully predict the system behavior in the frequency range of interest. These shortcomings consist of inability to i) predict monotonic instability due to real poles, ii) identify all electromechanical oscillatory modes, and iii)accurately predict damping (and consequently stability) of the oscillatory modes. This paper develops mathematical expressions to highlight the limitations of the complex torque coefficients method. Quantitative results based on three case studies, including a study on the first IEEE Benchmark System, are reported and results from eigenvalue analysis method, complex torque coefficients method, and time-domain simulation are presented and compared. This paper concludes that the complex torque coefficients method can be used only as a preliminary method for the investigation of torsional interactions and the results must be verified by other methods.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".