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Bidirectional DC-AC Converter Using a High- Frequency Transformer with Multi-Frequency Decoupled Power Control

2021· article· en· W3215941516 on OpenAlexaff
Juan Zuniga, Marius Takongmo, Chatumal Perera, Vishwa Perera, John Salmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransformerElectrical engineeringInverterForward converterIsolation transformerCapacitorElectromagnetic coilEngineeringElectronic engineeringVoltageControl theory (sociology)Computer scienceBoost converter

Abstract

fetched live from OpenAlex

A bidirectional isolated dc-ac converter that leverages the benefits of high-frequency (HF) power transfer to considerably reduce the size and weight of transformers is presented. The converter generates low-frequency grid compatible power output in a single conversion stage. Two sixleg inverters are connected across the open windings of three single-phase transformers, each with a center tap at the secondary. The inverters apply a differential-mode HF voltage across the windings to drive a HF current through the transformer, which is received by the secondary-side inverter to maintain the voltage of a dc capacitor. In addition, the secondary-side inverter generates a common-mode grid synchronized low-frequency voltage which drives the output current through the center tap of the secondary windings. A control scheme that independently operates on the HF power transfer and the low-frequency power output is presented. Simulation and experimental results of a 1.5-kW system are provided to validate the operation of the converter presented.

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 score0.005
Threshold uncertainty score0.017

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.009
GPT teacher head0.213
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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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