Identifying the presence of forced oscillations using oscillation signatures
Bibliographic record
Abstract
Forced oscillations in a power system are distinguished from natural oscillations as a rouge oscillatory input to the system. Two examples for sources of these forced oscillations are a malfunctioning governor that has not been modeled or a cyclic load with a low frequency. Power system oscillations are analyzed either by model based methods or by measurement based methods. Several recent papers have studied the impact of forced oscillations in estimating small signal stability of a power system using measurement based methods. When the frequency of the forced oscillation is in the vicinity of natural oscillations in the system, it has been reported that estimating natural and forced oscillatory modes is a challenge. This paper proposes utilizing model data to generate a database of oscillation signatures of the system for different forced oscillations of different frequencies at different origins and, aid measurement based oscillation monitoring methods to determine frequency and damping of natural oscillations and the source of forced oscillations, including at resonant conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".