Identification of dominant low‐frequency modes in ring‐down oscillations using multiple Prony models
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Bibliographic record
Abstract
This study presents a simple approach to modify the Prony algorithm to extract dominant low‐frequency modes present in ring‐down oscillations in power systems. The proposed approach is based on the observation that true modes present in the ring‐down oscillations appear consistently, irrespective of the order of the Prony model. It is shown that the consistently appearing modes can be extracted using a sorting method. The improved Prony algorithm which has the feature of extracting only the true modes present in the input signal is utilised to propose an oscillation monitoring algorithm in this study. The suitability of the proposed oscillation monitoring algorithm for real‐time monitoring of low‐frequency inter‐area oscillations is demonstrated using synthetic signals and simulated signals of different test systems.
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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 | 0.001 | 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 | 0.001 |
| 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 it