An Adaptive Method for DC Current Reduction in Totem Pole Power Factor Correction Converters
Bibliographic record
Abstract
Enabled by improved wideband gap semiconductor devices, the bridgeless totem pole power factor correction (PFC) converter is becoming an increasingly popular topology for front end ac input of high power battery chargers and telecom power supplies, achieving high efficiency as well as low electromagnetic interference. Unlike the conventional boost PFC, in a totem pole structure, different circuits are used for shaping the current in positive and negative half cycles. As a result, sensing inconsistencies may result in a significant dc component in the input current. In field applications, accumulation of dc currents from multiple PFC rectifier based loads can potentially lead to saturation of distribution transformers. This article proposes a low-cost method for adaptive detection and reduction of the dc input current based on time domain analysis of the dc link voltage. The effectiveness of the method in compensating manufacturing tolerances is experimentally validated on a 390 V, 1450 W interleaved totem pole PFC converter, and dc current reduction of up to 99.6% is achieved.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".