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Record W1486926650 · doi:10.1109/pedg.2015.7223026

Analysis and comparison of current-source-converter-based medium-voltage PMSG wind energy conversion systems

2015· article· en· W1486926650 on OpenAlexaff
Qiang Wei, Bin Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaximum power point trackingTotal harmonic distortionBoost converterCurrent sourcePulse-width modulationVoltage sourceWind powerMaximum power principleComputer scienceElectronic engineeringVoltageControl theory (sociology)Electrical engineeringEngineeringControl (management)

Abstract

fetched live from OpenAlex

CSC topology with natural advantages in terms of simple structure, grid friendly waveforms, controllable power factor, and reliable grid short-circuit protection is considered to be a promising converter configuration for use in mediumvoltage (kV) high-power (MW) PMSG based wind energy systems. In view of this, three types of configurations for current source converter (CSC) based medium-voltage PMSG wind energy conversion system (WECS) are investigated, analyzed and compared in this paper. The advantages and disadvantages of these configurations are illustrated. And detailed control scheme for each configuration is analyzed and conducted. Comparisons are carried out based on analysis and simulation results in terms of converter cost, operation range, generator-side current THD, maximum power point tracking (MPPT), dynamic performance, and number of control freedoms. Among all, the back-to-back pulse-width modulated (PWM) current source converter is considered to be the most promising converter. Additionally, it is further illustrated that the DC-current value determined by both generator-side and grid-side can ensure a full operation range under all operation conditions.

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 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 score0.909
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.031
GPT teacher head0.249
Teacher spread0.218 · 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.

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

Citations12
Published2015
Admission routes1
Has abstractyes

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