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Flux Minimization in Interphase Coupled Inductors of Parallel-Connected Voltage Source Converters

2022· article· en· W4280614076 on OpenAlexaff
Chenhui Zhang, Marius Takongmo, Vishwa Perera, John Salmon

Bibliographic record

Venue2022 IEEE Applied Power Electronics Conference and Exposition (APEC) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorInductanceConvertersInterphasePulse-width modulationVoltageElectromagnetic coilElectrical engineeringTopology (electrical circuits)Compensation (psychology)Materials scienceElectronic engineeringPhysicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

A flux compensation technique is described to significantly lower the peak flux in interphase inductors used to suppress the flow of high-frequency circulating currents between interleaved voltage source converters (VSCs) operating with the discontinuous pulse width modulation scheme (DPWM). The technique described is useful to reduce the required cross-sectional area of interphase inductors, which reduces the size, weight, and material cost of the magnetics in interleaved VSCs. Coupled inductors (CIs) with cross-coupled windings are used to demonstrate the performance of the flux compensation technique using simulation and experimental results of an 11 kW (300Vdc, 208Vac/31A) laboratory prototype. The inductor cross-coupled winding arrangement lowers the effective series inductance of the interphase inductors and makes it possible to produce high-frequency fundamental PWM output voltages with a very low voltage drop across the inductors.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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