Damping torque estimation and oscillatory stability margin prediction
Bibliographic record
Abstract
The damping torque of linearized models of power systems is studied here as a possible on-line security index, based on system identification techniques applied to realistic measurements. First, the theoretical values of damping and synchronizing coefficients of the electromagnetic torque are discussed in detail. These values are then used to investigate the accuracy of damping coefficient identified from on-line measurements using the ordinary least square (OLS) method. It is demonstrated that OLS may not be able to correctly estimate the coefficients due to the nonlinear nature of power system oscillations. Hence, generalized least square (GLS) and robust fitting with bisquare weights (RFBW) are applied to this system identification problem, showing to be better alternatives. Based on these results, the damping coefficient is proposed and studied as an index to calculate the distance to the closest oscillatory instability point. The results obtained from 3 test cases show that the index is an effective tool, and can be of significant help to operators for on-line security monitoring of power systems
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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 | 0.004 |
| 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 | 0.001 | 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".