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Record W2767523362 · doi:10.1109/ias.2017.8101738

A new inductive power transfer topology using direct AC-AC converter with active source current control

2017· article· en· W2767523362 on OpenAlexaff
Suvendu Samanta, Akshay Kumar Rathore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaximum power transfer theoremPower factorTopology (electrical circuits)Computer scienceElectronic engineeringInverterAC powerDirect currentPower (physics)Electrical engineeringVoltageEngineeringPhysics

Abstract

fetched live from OpenAlex

Generally, in wireless inductive power transfer (IPT) system the power is processed through multiple power transfer stages and this leads to lower efficiency and higher cost of the system. Recent research shows that the use of direct ac-ac converter in IPT system compensates these limitations significantly. However, one of the major challenge of IPT circuit with direct ac-ac converter is to achieve multiple control goals through a single converter. These include load power requirement, maintaining high quality source current and achieving soft-switching of inverter switches etc. In the existing literatures the research is more focused on meeting load power requirement and soft switching of inverter switches. This paper presents a new IPT topology using direct ac-ac converter topology. The control is carried out through conventional two loop method where the outer output voltage loop ensures load requirements and inner loop maintains high quality grid current. Maintaining output power factor of direct ac-ac converter in the lagging side, the ZVS turn on and ZCS turn off of two of the inverter switches are achieved. The detail analysis and design procedure of the converter is presented and experimental results are included to verify the analysis and proposed control.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.693
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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 teacher head, not a consensus.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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