Modeling and Control of a Single-Phase Sheppard-Taylor Based Power Factor Corrector
Bibliographic record
Abstract
In this paper, a single-phase power factor corrector (PFC) based on the Sheppard-Taylor topology is presented. Compared to conventional buck, boost or buck-boost PFCs, this topology allows a better current tracking at the AC side, with a relatively reduced voltage at the DC side. Consequently, the high frequency AC filters required by the buck PFCs are avoided, and the voltage stresses on the boost switches are significantly reduced. Furthermore, the control detuning phenomenon from which suffer most of the conventional PFCs, especially at very low input voltage, is avoided. This yields major improvements in the source current waveform. The proposed converter is integrated as a PFC at the DC-end of a single-phase diode bridge. A pulse-width-modulated (PWM) control is developed in order to ensure a unity power factor at the AC-source side and a regulated voltage at the DC-load side. In order to verify the performance of the proposed control scheme, simulation experiments are carried out on a numerical version of the converter with its control circuit. The implemented model of the converter is obtained by using the switching function technique. The control system is tested under both rated and disturbed operating conditions. The system performance is evaluated in terms of source current total harmonic distortion (THD), voltage regulation, robustness and dynamic time response to a set point offset.
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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 | 0.000 |
| 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 | 0.001 |
| 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".