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Record W4310179883 · doi:10.3390/electronics11233928

A Real-Time Maximum Efficiency Tracker for Wireless Power Transfer Systems with Cross-Coupling

2022· article· en· W4310179883 on OpenAlexafffund
Arpan Laha, Abirami Kalathy, Majid Pahlevani, Praveen Jain

Bibliographic record

VenueElectronics · 2022
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReactanceWireless power transferCoupling (piping)TransmitterPower (physics)Electronic engineeringMaximum power transfer theoremComputer scienceCapacitorPoint (geometry)Maximum power principleWirelessElectrical efficiencyControl theory (sociology)EngineeringElectrical engineeringPhotovoltaic systemVoltageChannel (broadcasting)MathematicsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This article proposes a real-time dynamic maximum efficiency tracking algorithm for wireless power transfer (WPT) systems with multiple receivers. The algorithm sequentially varies the net reactance of each of the receivers using switched capacitor circuits (SCCs) to reach the maximum efficiency point (MEP). The MEP in multiple-receiver systems varies in the presence of cross-coupling. This article provides an in-depth analysis of the effects of cross-coupling and proves that cross-coupling could be beneficial or detrimental to efficiency, depending on circuit conditions such as the load resistances and coupling factors among the coils. Hence, unlike previous research, this article emphasizes the improvement of the link efficiency in the presence of cross-coupling rather than the complete elimination of its effects. Experimental results have been included for a single-transmitter and two-receiver system to validate the feasibility of the proposed algorithm.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.006
GPT teacher head0.209
Teacher spread0.204 · 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 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

Citations1
Published2022
Admission routes2
Has abstractyes

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