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Record W2306103328 · doi:10.1109/tpwrd.2015.2492472

An Offshore Wind Generation Scheme With a High-Voltage Hybrid Generator, HVDC Interconnections, and Transmission

2015· article· en· W2306103328 on OpenAlexaff
Omid Beik, N. Schofield

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2015
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEngineeringElectrical engineeringOffshore wind powerElectric power systemConvertersTurbineTransmission systemWind powerPower electronicsElectricity generationVoltagePower (physics)Electronic engineeringTransmission (telecommunications)Mechanical engineering

Abstract

fetched live from OpenAlex

A new offshore high-voltage dc (HVDC) wind generation scheme is proposed in this paper. The scheme implements a high-voltage hybrid generator (HG) as well as HVDC interconnection and transmission systems. The turbine power train of the proposed system is compared with a typical system installed in a commercial wind farm. The analyses demonstrate improvements in system losses and, hence, efficiency, power-train hardware, including cable system mass and, importantly, a reduction in major component count and installed power electronics in the nacelle and turbine tower, features that lead to reduced capital cost and maintenance. The resulting power conversion system is more simplified and more amenable to higher voltage implementation since it is not constrained by existing state-of-art power-electronic voltage source converter structures. Voltage control is facilitated via dc/dc converters located away from the turbine tower. To demonstrate the HG operational concept, measured results from a low-power laboratory prototype HG system are compared with analytical results and show good agreement.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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)0.0010.001

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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 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

Citations40
Published2015
Admission routes1
Has abstractyes

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