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Record W2990128259 · doi:10.1109/ecce.2019.8911891

On the Efficiency of Series-Connected Offshore DC Wind Farm Configurations

2019· article· en· W2990128259 on OpenAlex
Marten Pape, Mehrdad Kazerani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOffshore wind powerConvertersSizingWind powerRectifier (neural networks)Marine engineeringElectrical engineeringWind speedVoltageSubmarine pipelinePower (physics)Series (stratigraphy)Computer scienceAutomotive engineeringEngineeringEnvironmental scienceMeteorologyPhysicsGeology

Abstract

fetched live from OpenAlex

Several past studies have shown that offshore wind farms with a series-connected DC collection system can have significant efficiency advantages over today's offshore wind farms with AC collection system and HVDC link. This has motivated the proposal of several power electronic converter configurations to implement such series-connected DC wind farms. However, the impacts of different configurations on efficiency and sizing characteristics are not well understood. This paper presents an efficiency study for two configurations featuring a diode-bridge rectifier-buck converter combination and voltage-source converter for 450MW wind farms located 100km away from shore. The loss modeling shows that the choice of converters and instantaneous wind speed differences within the farm have a significant impact on losses, as well as system operation and sizing, which in turn affect losses.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.656

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 communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.192
Teacher spread0.184 · 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

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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