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Record W3040302352 · doi:10.1109/oajpe.2020.3006352

Turbine Startup and Shutdown in Wind Farms Featuring Partial Power Processing Converters

2020· article· en· W3040302352 on OpenAlexafffund
Marten Pape, Mehrdad Kazerani

Bibliographic record

VenueIEEE Open Access Journal of Power and Energy · 2020
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOffshore wind powerConvertersTurbineWind powerPower optimizerEngineeringShutdownPower (physics)Electrical engineeringAutomotive engineeringMaximum power point trackingInverterVoltageMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

The wind turbine startup and shutdown procedures for electrical subsystems are straightforward for wind turbines featuring full-scale power electronic converters and have not required significant attentions from the research community. However, for offshore wind farms featuring series-connected dc collection systems and differential power processing capabilities, the controllable wind turbine power converters might not be rated in such a way that a full-scale output power difference can be handled within a series string. As startup and shutdown procedures can require a transition from zero to full power, a more elaborate design is required to ensure that wind turbines can still start and stop reliably under all conditions, without compromising advantages in reduced wind turbine converter ratings with differential power processing capabilities. In this paper, the startup and shutdown procedures for a series-dc wind farm featuring diode-bridge rectifiers and partial power processing converters as wind turbine converters, are investigated and constraints on converter ratings are evaluated.

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: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.030
GPT teacher head0.278
Teacher spread0.249 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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