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Record W2588642885 · doi:10.1109/ecce.2016.7854723

A new three-phase AC/DC high power factor soft-switched step-up converter with high gain rectifier modules for medium voltage grid in wind systems

2016· article· en· W2588642885 on OpenAlexaff
Mehdi Abbasi, John Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsYork University
Fundersnot available
KeywordsRectifier (neural networks)Power factorAC powerVoltageThree-phaseElectrical engineeringControl theory (sociology)Computer scienceElectronic engineeringEngineeringControl (management)

Abstract

fetched live from OpenAlex

This paper presents a new single-stage AC/DC step-up soft-switched converter for medium voltage (MV) DC grid for wind energy systems. To achieve close-to-unity power factor, stepping up the voltage and reduce the number of conversion stages, a three-phase boost AC/DC rectifier and a high frequency step-up resonant converter with high gain rectifier modules are combined into a single-stage step-up converter. As a result, the proposed converter is capable to achieve very high voltage gain efficiently without using bulky high turns ratio step-up transformer. Soft switching operation is achieved by using multiple resonant circuit modules. The voltage stress of each power switch is also reduced to half of the DC-link voltage by using a series connection of two strings of switch pairs. In addition, the proposed converter is capable to regulate the output MV grid voltage by utilizing a combination of duty cycle and variable switching frequency control. Results are given on a 1.5MW, 690Vac/40kV system to highlight the merits of the proposed converter.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations8
Published2016
Admission routes1
Has abstractyes

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