Experimental evaluation of active power factor correction techniques in a single‐phase AC‐DC boost converter
Bibliographic record
Abstract
Summary The increasing need to improve power quality with the reduction of the harmonic content of current and voltage waveforms has been intensively analyzed in several studies, thus motivating the proposal of many high power factor rectifiers based on the classic converters such as boost and buck‐boost. Moreover, distinct control techniques have also been proposed due to the commercial availability of integrated circuits (ICs) dedicated to impose sinusoidal input currents in switch‐mode power supplies (SMPSs). The boost converter operating in continuous conduction mode (CCM) is by far the most traditional choice for this purpose due to circuit simplicity and low electromagnetic interference (EMI) levels. Within this context, this work analyzes some of the most important control techniques used in power factor correction (PFC). The performance of a single‐phase boost converter using peak current mode control (PCMC), average current mode control (ACMC), and one cycle control (OCC) is evaluated experimentally in detail. A comprehensive analysis of key aspects such as the input current waveform and respective harmonic content, dc output voltage, and dynamic response of the converter is also presented.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".