Hybrid Single Phase Wide Range Amplitude and Frequency Detection with Fast Reference Tracking
Bibliographic record
Abstract
Amplitude, phase and frequency detection is key for synchronizing different AC sources. The most common and ever growing largely, usage for this technique is in grid interfaces for renewable sources. The determination of these variables are also useful for other power electronics applications, such as in Hybrid Power Amplifier (HPA), especially if it is digitally controlled. Several three-phase applications use variations of the Phase-Locked Loop (PLL) technique to determine the phase of the signals in order to apply the Clarke's Transformation to reduce the complexity of the system. Single-phase systems are more challenging since they require additional and more complex techniques to determine the phase. Usually, both single and three-phase systems are designed for a single and known frequency, usually the grid's frequency. However, a wider range of frequencies is necessary for other applications such as HPAs. In this paper, a hybrid solution for a single-phase signal amplitude and frequency detection with fast dynamics and wide input variation without prior knowledge of the frequency is proposed and evaluated experimentally. This solution enables the use of proper frequency, amplitude and phase values at the input of HPAs in order to improve the output quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".