Decoupled Current Balancing of a Digitally Controlled Interleaved Totem-Pole PFC Converter with High Computational Efficiency
Bibliographic record
Abstract
This paper proposes the design and implementation of a current balancing control scheme for digitally controlled totem-pole PFC converters, aiming to achieve efficient AC current sharing between the phase legs with a low additional computational cost. Based on nested voltage and input current loops associated with low bandwidth balancing loops, it is applied to a 3.3 kW GaN-based converter with three interleaved legs. Control decoupling is introduced to allow independent parallel operation of the loops. Digital implementation is presented and applied to two different resource-constrained MCUs, taking into account the phase shedding functionality. Computational load measurements show a saving of around 18% of MCU resources compared to the independent phase current control approach. Hardware-in-the-loop (HIL) testing is employed to verify good balancing against different types of imbalance contributions. Finally, experimental waveforms from a physical prototype confirm with excellent current balancing in operation. The fast dynamics of the input current regulation is maintained to guarantee the low harmonic distortion required in PFC applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".