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Record W3209362738 · doi:10.1109/tpel.2021.3123204

The Modular Current-Fed High-Frequency Isolated Matrix Converters for Wind Energy Conversion

2021· article· en· W3209362738 on OpenAlexaff
Yang Xu, Zheng Wang, Pengcheng Liu, Qiang Wei, Fujin Deng, Zhixiang Zou

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsLakehead University
Fundersnot available
KeywordsConvertersTransformerModular designWind powerEngineeringElectrical engineeringCapacitorVoltageGalvanic isolationElectronic engineeringComputer science

Abstract

fetched live from OpenAlex

In this article, a modular current-fed high-frequency transformer isolated matrix converters (CF-HFT-MC) based wind energy conversion system (WECS) is proposed. The CF-HFT-MC module utilizes the high-frequency transformer instead of the line-frequency input transformer, and eliminates the bulky and vulnerable electrolytic capacitors in the WECS. Therefore, the power density, reliability and efficiency of WECS can be increased. Moreover, single-stage power conversion and soft-switching can be achieved in the CF-HFT-MC modules, which can reduce the losses of the system. With the function of electric isolation, the wind turbines can be connected in series through the proposed CF-HFT-MC modules. Thus, the total dc output voltage is increased, which enables the offshore wind farm to be integrated to the current-source-converters based HVdc transmission directly. The modulation, control scheme and power sharing method are analyzed and designed for the proposed CF-HFT-MC based WECS in this article. Both the simulations and the experiments are given to verify the effectiveness of the proposed system.

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)
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 score0.982
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.214
Teacher spread0.207 · 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

Citations36
Published2021
Admission routes1
Has abstractyes

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