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Record W2119842654 · doi:10.1109/ecce.2011.6063819

Three-level boost converter based medium voltage megawatt PMSG wind energy conversion systems

2011· article· en· W2119842654 on OpenAlexafffund
Venkata Yaramasu, Bin Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersMaximum power point trackingRectifier (neural networks)Topology (electrical circuits)Wind powerCapacitorVoltageBoost converterControl theory (sociology)Computer scienceEngineeringElectronic engineeringElectrical engineeringControl (management)Inverter

Abstract

fetched live from OpenAlex

For power rating of 2MW or higher, the medium voltage (MV) back-to-back (BTB) neutral-point clamped (NPC) converters are most preferable choice for wind turbine manufacturers as they reduce cost, size and complexity of the system compared to the BTB two-level converters. In this paper, a new MV topology using diode rectifier, three-level boost (TLB) and NPC converter has been proposed to further reduce the cost and size. The dc-link maximum power point tracking (MPPT) control scheme has been proposed with which the TLB performs MPPT and balancing of dc-link capacitors and thus provides a greater flexibility for NPC control. The simulation results for 3MW/3000V/53.33Hz non-salient pole PMSG wind energy system validate the proposed topology and control scheme.

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 score0.992
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)0.0030.000

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 head0.037
GPT teacher head0.189
Teacher spread0.152 · 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

Citations65
Published2011
Admission routes2
Has abstractyes

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