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Record W2769554081 · doi:10.1109/ecce.2017.8096666

Efficiency improvement of three-phase LLC resonant converter using phase shedding

2017· article· en· W2769554081 on OpenAlexaff
Sayed Abbas Arshadi, Martin Ordonez, Mehdi Mohammadi, Wilson Eberle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConvertersResonant converterPhase (matter)Three-phasePower (physics)Modulation (music)Electronic engineeringPhase modulationControl theory (sociology)EngineeringComputer scienceElectrical engineeringPhysicsVoltagePhase noiseAcoustics

Abstract

fetched live from OpenAlex

One main advantage of three-phase LLC converters for high power applications is their full-load efficiency. However, issues with light load operation (i.e. low efficiency) requires investigation. In this paper, a new modulation strategy is proposed to improve the light load efficiency of three-phase LLC converters to achieve a better efficiency curve. The proposed strategy uses two different modulation techniques for the three-phase LLC converter: three phase and two phase modulations. Through these two operating modes, phase shedding for the three-phase LLC resonant converter is achieved. In order to validate the theoretical analysis, experimental results of a 3kW three-phase LLC resonant converter are provided. The results show light-load efficiency improvements of up to 2%.

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 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 score0.916
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.028
GPT teacher head0.310
Teacher spread0.282 · 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.

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

Citations19
Published2017
Admission routes1
Has abstractyes

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