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Fast Transient DC-Bus Dynamics in GaN-based PFCs: Dual-Loop Geometric Control

2024· article· en· W4396593651 on OpenAlexaff
Rahil Samani, Ignacio Galiano Zurbriggen, Matteo Sposito, Ignacio Santana

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInrush currentTransient (computer programming)ConvertersCapacitorController (irrigation)BusbarControl theory (sociology)Power factorElectronic engineeringComputer scienceVoltageElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Single-phase Power Factor Correction (PFC) converters require high DC-bus capacitances to cope with the double-line frequency power pulsation. This affects cost and size of the converter, and it creates high inrush currents. With the ongoing high-power-density trends in GaN-based converters, it is crucial to reduce the size of components, including DC-bus capacitors. This paper introduces a fast-transient geometric PFC controller on a GaN-based modified bridgeless converter capable of operating at 1 MHz switching frequency. The proposed controller is based on large-signal state-plane modeling and it addresses the sluggish dynamic performance of the conventional DC-bus voltage controller, enabling the utilization of a significantly reduced DC-bus capacitor. As a result, the inrush current is reduced and ceramic DC-bus capacitors can be employed, which increases the reliability, and it offers rapid response to load transients. The analysis of this paper is supported by mathematical derivations and validated via simulations and experimental results.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.004
GPT teacher head0.198
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

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