An Equivalent Dynamic Phasor Model for a Single-Phase Boost Power-Factor-Correction Converter
Bibliographic record
Abstract
To mitigate harmonic current flow in distribution systems, single-phase diode-bridge rectifiers (DBRs) are commonly equipped with active power factor correction (PFC) controllers. Achieving high power quality and dynamic performance in PFC controller design demands a precise understanding of PFC converter behavior. While detailed electromagnetic transient (EMT) simulations provide accurate insights, they are time-consuming. To address this, the dynamic phasor (DP) method offers a more efficient modeling approach for power converters. This paper introduces and explores the DP model of a single-phase boost PFC converter, along with guidelines to integrate it with existing simulation platforms. To overcome challenges arising from differing driving frequencies (line frequency for the DBR and switching frequency for the DC-DC converter) in the DP modeling of boost PFC converters, we employ the sign function to transform the dynamic model of the single-phase DBR and boost DC-DC converter into an equivalent single-phase active rectifier model. Subsequently, we convert this equivalent model into a DP model for the single-phase boost PFC converter. Utilizing small-signal analysis, we establish a systematic design procedure for using the proposed DP model to tune and optimize PFC controller gains. Simulations conducted in MATLAB/Simulink, along with error calculations, demonstrate a strong correlation between the proposed DP model and the detailed EMT model results, while also highlighting significant numerical simulation advantages of the DP model over the detailed EMT model. Experimental results further validate the practical utility of the proposed DP model in tuning control systems for single-phase boost PFC converters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".