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Record W4380303595 · doi:10.1109/tie.2023.3283700

An Improved Current-Source-Converter-Based Series-Connected Wind Energy Conversion System

2023· article· en· W4380303595 on OpenAlexaff
Ling Xing, Qiang Wei, Ryan Li

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsLakehead UniversityUniversity of Alberta
Fundersnot available
KeywordsCurrent (fluid)Wind powerSeries (stratigraphy)Current sourceComputer scienceElectrical engineeringElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

The passive-rectifier-based current source converter (CSC) used in the series-connected wind energy conversion system features low cost, high reliability, and simple control, but it suffers from highly distorted generator stator currents. A new CSC-based wind energy conversion system composed of a multiphase generator, a passive rectifier, a modular medium-frequency transformer-based converter, and a CSC is proposed. In addition, two versions of the proposed converter are being developed for low- and medium-voltage turbine systems, respectively. The operation principle is presented, controls are developed, and simulations and experiments are conducted. Both converters offer excellent generator stator current harmonic performance while also retaining the advantages of the existing CSC-based 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 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)
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 score0.892
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.217
Teacher spread0.197 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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