Fast estimation of power system frequency using adaptive internal-model control technique
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Bibliographic record
Abstract
This paper presents a new approach to the fast estimation of power system frequency. The approach is adopted from the application of internal model based periodic disturbance cancellation technique in the control field. Frequency can be estimated by feeding the power system signals into a control system with an internal model incorporated in the feedback loop. After the first about 20 ms convergence, the approach is able to provide accurate noise-free estimates in less than 10 ms despite the presence of harmonics and DC decay. The estimation performance has been improved by 50 percent by introducing a notch filter to the adaptation loop. Computation requirements for the proposed method are very low compared to existing methods. The design of the control system is also described in the paper. Simulations are conducted using computer synthesized signals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 it