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Frequency-Dependent Electrical Characteristics of Submarine Cables in Low Frequency High Voltage ac (LF-HVac) Transmission for Offshore Wind

2023· article· en· W4387005586 on OpenAlexaff
Okechukwu Efobi, Wei Li, Mukesh Kumar Das, A.M. Gole

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHVACElectrical engineeringOffshore wind powerEngineeringHigh-voltage direct currentTransmission (telecommunications)Power transmissionMaximum power transfer theoremElectric power systemVoltageSubmarineTransmission systemPower (physics)Automotive engineeringMarine engineeringWind powerAir conditioningMechanical engineeringDirect currentPhysics

Abstract

fetched live from OpenAlex

Low frequency high voltage ac (LF-HVac) transmission has been proposed as a bulk power transfer alternative to either the conventional 50/60 Hz HVac or the high voltage dc (HVdc) schemes. In LF-HVac transmission, the suggestion is to use fractional values of the conventional 50/60 Hz as the ac operating frequency. Doing so would considerably increase the power transfer capacity of a given transmission cable, extend the range, reduce losses, and improve voltage and dynamic stability of the system. Grid integration of remote offshore wind farms has been identified as the most promising potential application of LF-HVac. Thus, this paper presents a study on the electrical characteristics and performance of submarine cables with respect to the operating frequency. The findings indicate that LF-HVac operation of a cable system would achieve superior power handling capacity and efficiency when compared to HVac operation of the same cable system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.741

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 integrity0.0000.000
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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