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Record W2020469728 · doi:10.1109/ievc.2014.7056229

Boucherot Bridge based zero reactive power inductive power transfer topologies with a single phase transformer

2014· article· en· W2020469728 on OpenAlexaff
Konrad Woronowicz, Alireza Safaee, Tim Dickson, Mohamed Z. Youssef, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech UniversityBombardier (Canada)
Fundersnot available
KeywordsLeakage inductanceInductanceElectrical engineeringMaximum power transfer theoremTransformerEnergy efficient transformerNetwork topologyElectromagnetic coilDistribution transformerDelta-wye transformerEngineeringIsolation transformerElectronic engineeringPower electronicsTransformer typesAC powerTopology (electrical circuits)Computer scienceVoltagePower (physics)Physics

Abstract

fetched live from OpenAlex

High reliability and cost effectiveness of power electronics components has resulted in very high interest in inductive power transfer techniques among academic and industrial professionals. Since the major interest is directed towards automotive industry for battery charging, a special transformer with a large gap and relatively large leakage inductance is used. In order to achieve desired voltage levels high frequencies in the order of 20 to 100 kHz are preferred, producing high levels of reactive power which if not compensated limits power and efficiency. Thus, there is a need to tune or compensate the significant inductance of the windings. There are four basic transformer tuning topologies reported in literature, each one being a combination of series and/or parallel. This paper presents all four topologies and associated input to output dependences based on a Boucherot Bridge model of a transformer.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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