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Record W2108066623 · doi:10.1109/epec.2010.5697183

A hybrid HVDC transmission system supplying a passive load

2010· article· en· W2108066623 on OpenAlexafffund
Omar Kotb, Vijay K. Sood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRectifier (neural networks)Transmission systemElectric power systemInverterEngineeringTotal harmonic distortionFault (geology)EmtpTransmission lineElectronic engineeringElectric power transmissionHVDC converterComputer scienceElectrical engineeringTransmission (telecommunications)VoltagePower (physics)Transformer

Abstract

fetched live from OpenAlex

The operational characteristics of a Voltage Source Converter (VSC)-HVDC transmission system make it a versatile asset in modern power systems. The advantages of the new technology are somewhat offset by some drawbacks, such as the high power losses, equipment insulation stresses, and relatively high cost. A hybrid Line Commutated Converter (LCC)-VSC HVDC transmission system combines the benefits of both conventional LCC and new VSC technologies. In this paper, a hybrid HVDC system is used to supply a passive AC network. The control systems for rectifier and inverter are discussed, along with additional control schemes for starting, load shedding, and potential AC network fault situations. The operational characteristics of the hybrid system under selected control modes are validated by EMTP-RV simulation under both steady state and transient conditions such as load shedding and AC faults. Finally, a brief performance analysis of transmission efficiency and harmonic distortion is presented.

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations36
Published2010
Admission routes2
Has abstractyes

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