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Record W2485140875 · doi:10.1109/pedg.2016.7527098

High-efficiency RB-IGBT based low-voltage PWM current-source converter for PMSG wind energy conversion systems

2016· article· en· W2485140875 on OpenAlexaff
Jianwen Zhang, Peiyuan Li, Jiacheng Wang, Xu Cai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInsulated-gate bipolar transistorPulse-width modulationConvertersBlocking (statistics)Electrical engineeringVoltageCurrent injection techniquePower (physics)Power semiconductor deviceEnergy conversion efficiencyTransistorElectronic engineeringEngineeringBipolar junction transistorComputer sciencePhysics

Abstract

fetched live from OpenAlex

A low-voltage (LV) pulse-width modulated current-source converter (CSC) using reverse-blocking insulated gate bipolar transistor (RB-IGBT) devices is proposed in this paper for megawatt wind energy conversion systems (WECSs) with a permanent magnet synchronous generator. Benefiting from using the latest generation of reverse-blocking power semiconductors, the presented configuration is able to push the switching frequency to a higher range and overcome the traditional drawback of low efficiency in LV CSCs. Design of the configuration, switching scheme, and system control are briefly introduced. Semiconductor and converter loss models are developed for detailed efficiency study of the proposed system. The overall high-efficiency performance of the LV CSC based WECS is verified by simulation results and comparison with the state-of-the-art solution using voltage-source converters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 designBench or experimental
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

Citations17
Published2016
Admission routes1
Has abstractyes

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