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Record W4399768572 · doi:10.1109/tie.2024.3404129

Soft Switched High Step-Up Multi-Port Converter With Single Magnetic Core and Auxiliary Switch for Renewable Energy Applications

2024· article· en· W4399768572 on OpenAlexaff
Erfan Meshkati, Mohsen Packnezhad, Hosein Farzanehfard, S. Ali Khajehoddin

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRenewable energyElectronic engineeringMagnetic coreInductorElectrical engineeringCore (optical fiber)Computer scienceElectromagnetic coilEngineeringTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

This article presents a nonisolated high step-up soft switched multiport converter (MPC) based on the boost three port (BTP) structure with a single magnetic core. To attain high voltage gain, coupled inductors technique is utilized while an auxiliary circuit including only a switch, a capacitor along with three windings coupled with the main inductor are utilized to absorb the leakage inductance energy, provide soft switching and increase the voltage gain. The BTP structure main switch and added auxiliary switch operate at zero-voltage switching while all diodes turnoffat zero current switching. Moreover, all switches are clamped to relatively low voltages to reduce their voltage stresses. Utilizing a single switch auxiliary circuit in all operating modes and employing a single magnetic core have considerably improved the converter efficiency, component count, and volume. A 200 W, 400 V converter prototype is implemented to validate the proposed MPC performance and a comprehensive feature comparison is performed with other counterpart converters.

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 communication0.0000.001
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 head0.023
GPT teacher head0.225
Teacher spread0.203 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

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