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Record W2150146385 · doi:10.1109/pesc.2007.4342160

Analysis of a Virtual Air Gap Variable Reactor

2007· article· en· W2150146385 on OpenAlexaff
Dale Dolan, Peter W. Lehn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReactanceThyristorAir gap (plumbing)HarmonicsControl theory (sociology)Variable (mathematics)Integrated gate-commutated thyristorEngineeringElectromagnetic coilPower (physics)VoltageThyristor driveElectrical engineeringComputer sciencePhysicsControl (management)Materials science

Abstract

fetched live from OpenAlex

This paper presents the basic operation of a virtual air gap variable reactor (VAG-VR). Variable reactors have many applications in the power industry. Their use allows control of line power flow, as well as damping of power oscillations and subsynchronous resonances. A variable reactor is most commonly implemented as a thyristor controlled reactor (TCR) by switching in and out a constant reactance to achieve an averaged variable reactance. By using a virtual air gap, implementation of a continuously variable reactance is possible without introducing the harmonics created by the thyristor switching. A high speed of response is achieved by using a power electronics solution to drive the DC control windings. A full bridge dc-dc converter is used to provide the full range of negative and positive voltage required.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations26
Published2007
Admission routes1
Has abstractyes

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