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Record W2957159498 · doi:10.1109/tte.2019.2927845

Field-Oriented Control of a Three-Phase Wireless Power Transfer System Transmitter

2019· article· en· W2957159498 on OpenAlexafffund
Mehanathan Pathmanathan, S. Nie, Netan Yakop, Peter W. Lehn

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2019
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransmitterWireless power transferElectrical engineeringRectifier (neural networks)Electromagnetic coilStatorCapacitorMaximum power transfer theoremVoltageEngineeringElectronic engineeringComputer sciencePower (physics)PhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper presents a method of designing a three-phase wireless power transfer (WPT) transmitter analogous to the stator windings of a three-phase, two-pole electrical machine. A method of deriving the required transmitter coil currents based on lateral receiver misalignment is presented, wherein the transmitter currents are decomposed into direct and quadrature axis components. This decomposition simplifies the derivation of the transmitter currents required to minimize copper losses for a wide range of receiver misalignment by reorienting the transmitter magnetic field toward the receiver. Based on the required transmitter current, the required series compensation capacitors and transmitter voltage sources needed for unity power factor operation are calculated. Simulation results are shown for a 3.3-kW system utilizing a receiver-side voltage-doubling rectifier and 300-V battery. Finally, experimental results on a 1-kW prototype are shown, where a coil efficiency of 95.17% at perfect receiver alignment and 90.52% at 20-cm lateral misalignment was measured.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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

Citations28
Published2019
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Transportation ElectrificationSame topicWireless Power Transfer SystemsFrench-language works237,207