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Record W1994995915 · doi:10.1109/tpel.2010.2076365

Three-Phase (LC)(L)-Type Series-Resonant Converter With Capacitive Output Filter

2010· article· en· W1994995915 on OpenAlexaff
M. Almardy, Ashoka K. S. Bhat

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBuck–boost converterForward converterNegative impedance converterBoost converterĆuk converterFlyback converterLC circuitElectronic engineeringEngineeringResonant inverterTransformerIntegrating ADCCapacitive sensingControl theory (sociology)VoltageInverterElectrical engineeringCapacitorComputer science

Abstract

fetched live from OpenAlex

This paper presents a three-phase (LC)(L)-type dc-dc series-resonant converter with capacitive output filter. Operation of the converter has been presented using the operating waveforms and equivalent-circuit diagrams during different intervals. An approximate analysis is used, and design procedure is presented with a design example. Intusoft simulation results for the designed converter are given for input voltage and load variations. Experimental results obtained with a 300-W converter are presented. Major advantages of this converter are the leakage and magnetizing inductances of the high-frequency transformer that are used as a part of resonant circuit, and the output-rectifier voltage is clamped to the output voltage. Also, the converter operates in soft switching for the inverter switches with a narrow frequency control range and the tank current decreases with the load current.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 communication0.0000.000
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 head0.007
GPT teacher head0.214
Teacher spread0.208 · 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".

Quick stats

Citations44
Published2010
Admission routes1
Has abstractyes

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