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Record W4378195005 · doi:10.1109/tpel.2023.3279822

A Discrete Coupled Multiphase Interleaved LLC Converter With Symmetrical Components Analysis

2023· article· en· W4378195005 on OpenAlexaff
Xiang Zhang, Shangzhi Pan, Praveen Jain

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsInductanceConvertersInductorElectrical impedanceTopology (electrical circuits)Electronic engineeringSequence (biology)Equivalent circuitComponent (thermodynamics)Three-phaseElectronic componentCoupling (piping)Phase (matter)Computer scienceVoltageElectrical engineeringEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

This article proposes a coupling structure and a generalized analysis approach for multiphase interleaved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converters that applies to all odd phases that can simultaneously improve the current balancing in all interleaved phases. First, a discrete coupled inductor array (DCIArray) structure is proposed, and its magnetic circuit model is analyzed in detail; then, the generalized symmetrical component theory is introduced to decouple the inductance matrix to the sequence impedance, with expansion to all odd-phase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converters, and then, the sequence impedance, coupling coefficient, components tolerance impacts, and extended voltage gain with control architecture are discussed under the corresponding models; finally, the recommended design for three-phase and five-phase DCIArray was discussed. Experiments based on both three-phase and five-phase prototypes have proved that the proposed scheme has excellent current sharing in almost all frequency ranges.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.227
Teacher spread0.218 · 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
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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207