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Record W2168392873 · doi:10.1109/pes.2006.1709469

DC bus control of variable speed wind turbine using a buck-boost converter

2006· article· en· W2168392873 on OpenAlexaff
Tahar Tafticht, Kodjo Agbossou, A. Chériti

Bibliographic record

Venue2006 IEEE Power Engineering Society General Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsTurbinePower optimizerWind speedPermanent magnet synchronous generatorMaximum power principleMaximum power point trackingWind powerBuck converterControl theory (sociology)Power (physics)Automotive engineeringEngineeringComputer scienceElectrical engineeringMagnetControl (management)Photovoltaic systemMechanical engineeringPhysicsVoltageMeteorology

Abstract

fetched live from OpenAlex

For most of the peak power extraction methods in wind turbine generation system described in the current literature, it is necessary to know the wind turbine's maximum power curve and the wind speed measurement. These methods used the maximum power curve obtained via simulations or tests for individual wind turbines. This makes these methods difficult and expensive to implement in practice. In addition, the use of wind speed sensor to measure the wind speed adds to a system a cost and presents some difficulties in practical implementation. This paper describes the design of a buck-boost converter circuit used to achieve the maximum power control of wind turbine driven permanent magnet synchronous generator (PMSG). The PMSG is suitably controlled according to the generator speed and thus the power from a wind turbine settles down on the maximum power point by the proposed MPPT control method, where the wind turbine's maximum power curve and the information on wind velocity are not required

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 score0.003
Threshold uncertainty score0.012

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 science0.0000.000
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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations52
Published2006
Admission routes1
Has abstractyes

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