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Record W2769672114 · doi:10.1109/ecce.2017.8095755

A ZVT cell for high-frequency quasi-resonant converters in ON-OFF mode for solar applications

2017· article· en· W2769672114 on OpenAlexaff
Hossein Mousavian, Alireza Bakhshai, Praveen Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsConvertersElectronic engineeringModulation (music)Pulse-width modulationRLC circuitVoltagePower (physics)PhysicsComputer scienceControl theory (sociology)Electrical engineeringEngineeringCapacitorControl (management)

Abstract

fetched live from OpenAlex

In this paper, a fully ZVT auxiliary cell for boost type resonant converters is proposed to achieve soft switching in ON-OFF control mode. In conventional ON-OFF methods, transient and switching losses at the beginning and end of each power pulse decrease the overall efficiency of the system at high modulation frequencies. This ZVT cell offers zero voltage switching without imposing extra resonant current. This structure can be implemented in most boost type resonant converters such as class E, φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and quasi-resonant DC-DC converters. The fully ZVS operation, the absence of inductive components, and low voltage and current stress are main advantages of this cell. Thus, the ON-OFF control can be implemented at higher modulation frequencies without any significant drop in the efficiency. Simple structure and timing are other advantages of the proposed cell, which makes it useful for solar applications. The operation of the proposed auxiliary cell is analyzed in individual intervals. To verify the mathematical analysis, the proposed cell is implemented in a 3 MHz quasi-resonant boost converter. A maximum efficiency of 95.8% is obtained at 385 watts output and the efficiency drops about two percent at 80 watts at 500 kHz modulation frequency.

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: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.789

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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
GenreMethods

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

Citations4
Published2017
Admission routes1
Has abstractyes

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