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Record W4392903253 · doi:10.1109/tie.2024.3368100

Comprehensive Study and Performance Evaluation of an Interleaved GaN-Based PFC With Magnetic Component Size Reduction

2024· article· en· W4392903253 on OpenAlexafffund
Jalal Dadkhah, Carl Ngai Man Ho, Jimmy Xuechao Liu, Yanming Xu

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Manitoba
FundersMitacs
KeywordsComponent (thermodynamics)Reduction (mathematics)Materials scienceElectronic engineeringOptoelectronicsComputer scienceMathematicsEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Power factor correction (PFC) converters can be significantly reduced in size by integrating control circuits, sensing circuits, gate drivers, and power switches. However, magnetic components are the main parts that are bulky and prevent further PFC size reduction. Recently, a highly integrated commercial PFC called Gallium Nitride-controller module (GaN-CM) was introduced, which employs a novel controller in discontinuous conduction mode (DCM) to reduce the size of the boost inductor. The question remains, however, of how to quantify the inductor size reduction while maintaining high efficiency. Moreover, the power rating of GaN-CM is up to 240 W with a single-module operation. In this article, steady-state analysis, loss analysis, and inductor optimization of GaN-CM are conducted to show how DCM PFC can lead to a small inductor with high efficiency. Furthermore, GaN-CM interleaving is proposed to increase the power rating of GaN-CM. The proposed interleaved PFC is verified by a 400-W prototype with an advanced GaN transistor.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.037
GPT teacher head0.278
Teacher spread0.241 · 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.

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

Citations5
Published2024
Admission routes2
Has abstractyes

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