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Record W2113238929 · doi:10.1109/tpel.2011.2106143

A Low-Cost Rectifier Topology for Variable-Speed High-Power PMSG Wind Turbines

2011· article· en· W2113238929 on OpenAlexaff
Jiacheng Wang, Dewei Xu, Bin Wu, Zhenhan Luo

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2011
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRectifier (neural networks)Permanent magnet synchronous generatorMaximum power point trackingWind powerControl theory (sociology)Precision rectifierThyristorMaximum power principleTopology (electrical circuits)Wind speedPower (physics)VoltageGenerator (circuit theory)EngineeringComputer scienceElectrical engineeringPower factorPhysicsControl (management)

Abstract

fetched live from OpenAlex

A novel rectifier topology consisting of two three-phase diode bridges and three thyristors is proposed in the paper for variable-speed high-power permanent-magnet synchronous generator (PMSG) wind energy conversion systems (WECSs). The proposed rectifier has several prominent features such as low cost, low power loss, and simple control. Its ability to cascade the input voltages allows it to properly regulate generator speed even when the wind velocity drops to half of the rated value. Consequently, maximum power-point-tracking algorithms can be applied to optimize power capture in a wide range of wind velocities. The operating principle of the rectifier is elaborated. Its use and control in the WECS is presented. The converter and control are verified by simulation and experimental results.

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 score0.006

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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

Citations93
Published2011
Admission routes1
Has abstractyes

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