Real-time testing of Newton-phaselet method for calculating the power factor of single phase loads
Bibliographic record
Abstract
A combination of Newton iterations and phaselet tight frames allows calculating the power factor of a single phase load. In this paper, the real-time implementation and experimental testing of the Newton-phaselet method are presented. The tested method is structured to employ the Newton iterations in order to estimate values for the apparent power S, and to utilize phaselet tight frames to calculate an angle v for the estimated S at each iteration. The estimated S and calculated v at each iteration provide a numerical value for the active power P. This calculated value of P is compared to the measured one in order to determine the required adjustment in S for the next iteration. The Newton-phaselet method is implemented in real time by using a digital signal processing board, where the measured active power is fed as the input. Experimental performances of the Newton-phaselet method are investigated for single phase linear, non-linear, and inverter-fed loads supplied at different frequencies. Test results demonstrate high accuracy, simple implementation, low memory requirements, fast convergence, and negligible sensitivities to harmonic components and supply frequencies.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".