Frequency Locked Phase Estimation Under Harmonically Distorted Conditions
Bibliographic record
Abstract
A novel method is proposed for the estimation of the fundamental component of harmonically distorted power line voltage. Common estimation techniques involve a multiplication of the line voltage signal with an estimate of the fundamental line voltage component, to measure the discrepancy between the line phase and the estimated phase, which results in undesired frequency components in the phase error signal. These new frequency components cause ripple in the output phase estimation, which can typically be reduced by low-pass filtering following the phase detection, at the expense of slowing down the overall dynamic response of the synchronization process. To address the harmonic distortion in the line voltage signal, the proposed method measures the phase error by directly subtracting the incoming signal from a one-cycle delayed copy of this same signal. This error signal is then used to adapt the sampling rate to store exactly one cycle of the input signal. For a harmonically distorted signal, this produces a zero steady state phase error signal, and a ripple-free sampling frequency. This sampling clock signal serves to operate a fixed discrete frequency quadrature signal generator, used to perform a sliding correlation of the input signal with the quadrature signals, to extract an estimate of the line voltage fundamental component. The principal feedback loop in the proposed method thus aims only at frequency tracking, resulting in faster overall response. Furthermore, due to notching distortion commonly encountered in power systems, an additional mechanism is proposed to attenuate the impact of such distortion. Simulation results are presented to validate this proposed method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".