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Record W1480584756

Six-phase PMSG wind energy conversion system based on medium-voltage multilevel converter

2011· article· en· W1480584756 on OpenAlexaff
Mario J. Durán, Samir Kouro, Bin Wu, E. Levi, Federico Barrero, Salvador Alepuz

Bibliographic record

VenueEuropean Conference on Power Electronics and Applications · 2011
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPermanent magnet synchronous generatorBoost converterVoltage sourceConvertersTopology (electrical circuits)Electrical engineeringThree-phaseVoltageBuck–boost converterComputer scienceElectronic engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

A new power converter interface for permanent magnet synchronous generator (PMSG) based wind energy conversion systems (WECS) is presented in this paper. The converter stage is capable to step-up the low-voltage side of standard six-phase PMSG used in today's WECS to a medium-voltage grid side. The proposed converter is an hybrid back-to-back topology composed of two series connected three-phase voltage source converters (VSC) at the generator side, and a 3-level neutral point clamped converter (NPC) at the grid side. Operating with a multilevel converter at medium voltage at the grid side has several advantages like improved power quality, increased efficiency, easier grid code compliance and smaller cables to name a few. Compared to a back-to-back NPC configuration at both ends, the proposed topology does not require a medium voltage generator. The feasibility of the proposed configuration is verified by simulation analysis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.214
Teacher spread0.193 · 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 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

Citations72
Published2011
Admission routes1
Has abstractyes

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