A nonlinear adaptive filter for online signal analysis in power systems: applications
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents various applications of a nonlinear adaptive notch filter which operates based on the concept of an enhanced phase-locked loop (PLL). Applications of the filter for online signal analysis for power systems protection, control and power quality enhancement are presented. The proposed scheme can be applied for signal analysis both under stationary and nonstationary conditions. Based on digital time-domain simulations, applications of the filter for (a) sinusoidal waveform peak detection, (b) harmonic identification/detection, (c) detection/extraction of individual components of a signal, (d) instantaneous reactive current extraction, (e) disturbance detection, (f) noise reduction in zero-crossings detection, and (g) amplitude (phase) demodulation for flicker estimation, are presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 it