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An Inductive Power Transfer System using Soft-Switched AC/AC Active-Clamped Half-Bridge Converter with Predictive Dead-Beat Grid Current Control

2020· article· en· W3096831396 on OpenAlexaff
Phuoc Sang Huynh, Deepak Ronanki, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCapacitorInductanceMaximum power transfer theoremElectrical engineeringWireless power transferPower factorComputer scienceVoltageAC powerElectronic engineeringEngineeringControl theory (sociology)Power (physics)PhysicsElectromagnetic coilControl (management)

Abstract

fetched live from OpenAlex

In this paper, a dead-beat control-based AC/AC active-clamped half-bridge (HB) converter feeding inductive wireless charging system is presented. It offers some advantages of a low component count, high efficiency, reliability, cost-effectiveness, and unity power factor. The system design procedure and non-linear dead-beat controller development are given in detail. The main drawbacks of the AC/AC active-clamped HB converter are the high current stress and hard switching of the lower switches. In this paper, the all-switch zero-voltage-switching (ZVS) operation of the AC/AC active-clamped HB converter based inductive wireless charging system is explored. Moreover, the performance of the charging system under the variation of the mutual inductance and compensation capacitors are evaluated. Simulation and experimental results are given to verify the theoretical analysis.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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".

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Citations7
Published2020
Admission routes1
Has abstractyes

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