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Design and Control of High Performance High Efficiency Totem-Pole PFC for Data Center Power Supply

2025· article· W7138115582 on OpenAlexaff
Quoc Nam Trinh, Thanh Hai Nguyen

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsControl (management)Power (physics)Control systemPower controlCenter (category theory)Measure (data warehouse)

Abstract

fetched live from OpenAlex

This paper proposes a novel control strategy for a bridgeless totem-pole boost power factor correction (PFC). The proposed control scheme is developed with 2-poles-2-zeros compensator for both DC-link voltage control loop and current control loop. The proposed control scheme improves dynamic response of the totem pole PFC with load change without degrading the input current performance. In addition, a novel AC input voltage lost detection is developed to guarantee a proper control of totem pole PFC when AC input lost and recovered. The proposed detection method can detect AC loss as fast as 500us to ensure no high current spike or overcurrent occur during the event. Experimental tests of 3.6 kW totem-pole PFC prototype verify the effectiveness of the proposed control scheme.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.259
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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