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A High-frequency Compact Zero-Voltage-Transition GaN-based Single-phase Inverter

2022· article· en· W4280488277 on OpenAlexaff
Mohammadreza Hazrati Karkaragh, Morteza Esteki, Mohammad Reza Mohammadi, S. Ali Khajehoddin

Bibliographic record

Venue2022 IEEE Applied Power Electronics Conference and Exposition (APEC) · 2022
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorInverterVoltageResonant inverterPower (physics)Electronic engineeringElectrical engineeringMaterials scienceComputer scienceTopology (electrical circuits)EngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a high-efficiency soft-switching GaN-based single-phase inverter composed of a full-bridge DC-AC inverter and an auxiliary circuit. The auxiliary circuit incorporates two active switches and an inductor coupled with the filter inductor. The auxiliary circuit does not add any components in the main power path, and no isolated gate driver is needed. Besides, using a coupled-inductor in the auxiliary circuit reduces the voltage stress on the switches and enables the utilization of GaN switches. Moreover, a variable-timing method controls the auxiliary circuit and reduces the conduction losses in the auxiliary switches. As a result, the efficiency and power density are improved. The proposed inverter is analyzed, and experimental results of a 200kHz, 500W prototype converter are reported.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.212
Teacher spread0.200 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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