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Record W2152811999 · doi:10.1109/pesc.2008.4592438

Multi-level single phase boost rectifiers using coupled inductors

2008· article· en· W2152811999 on OpenAlexaff
John Salmon, Andrew M. Knight, Jeffrey Ewanchuk, N. I. Mohd Noor

Bibliographic record

VenuePESC record · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorRippleConvertersPulse-width modulationVoltageWaveformElectronic engineeringBoost converterRectifier (neural networks)Magnetic coreComputer scienceElectrical engineeringEngineeringControl theory (sociology)Electromagnetic coil

Abstract

fetched live from OpenAlex

The number of voltage levels available in PWM voltage source converters can be increased by using a split-wound coupled inductor and interleaved PWM switching. This paper examines this technique using a multi-level 1-phase boost rectifier. Traditional interleaved boost rectifiers lower the high frequency input current ripple using current ripple cancellation. Alternatively, the proposed approach uses high frequency multi-level PWM voltage waveforms. The high frequency current ripple and parasitic DC currents are the only winding current components that produce magnetic flux in the coupled inductors. This contrasts with traditional interleaved boost rectifiers where the much larger AC supply input current produces flux in the magnetic core. This feature allows the physical size of the coupled inductors to be reduced, and their inductor value increased to provide improved protection against core saturation. Simulation and experimental results are used to illustrate the operation of the proposed converters. A design study is presented to illustrate that the proposed technique has much lower magnetic core losses, and is competitive in size to traditional techniques.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.136
GPT teacher head0.289
Teacher spread0.154 · 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

Citations21
Published2008
Admission routes1
Has abstractyes

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