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Record W2159519367 · doi:10.1109/pes.2008.4595983

Harmonic mitigation in a Virtual Air Gap Variable Reactor via control current modulation

2008· article· en· W2159519367 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
KeywordsHarmonicsHarmonicReactanceModulation (music)ThyristorAir gap (plumbing)Current (fluid)Electronic engineeringHarmonic analysisControl theory (sociology)EngineeringVoltageComputer scienceElectrical engineeringMaterials sciencePhysicsAcousticsControl (management)

Abstract

fetched live from OpenAlex

A Virtual Air Gap Variable Reactor is a device that is capable of producing a continuously variable reactance with a better dynamic response and without introducing the harmonics created by the thyristor switching of a TCR. This paper presents a method of harmonic mitigation that can be applied to a laboratory prototype Virtual Air Gap Variable Reactor (VAG-VR) which further improves on the low harmonics already achieved. The method utilizes control current modulation such that the DC auxiliary current is modulated with a 2nd harmonic component. It is seen that the 3rd harmonic can be reduced in the range of 60% - 96% as compared to using the DC auxiliary current without modulation. The 5th and 7th harmonics also show a modest reduction. This improvement is in addition to the improvements that have already been achieved by the VAG-VR as compared to the thyristor controlled reactor (TCR).

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

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