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Record W2433052068 · doi:10.1109/tpwrd.2006.886798

A Tap-Changing Algorithm for the Implementation of “Sen” Transformer

2007· article· en· W2433052068 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2007
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTap changerTransformerFortranQuadrature boosterAC powerAutotransformerEngineeringElectric power systemPower flowComputer scienceElectronic engineeringVoltageControl theory (sociology)AlgorithmElectrical engineeringDistribution transformerPower (physics)

Abstract

fetched live from OpenAlex

The "Sen" transformer (ST) is made out of a transformer and tap changers and is capable of regulating the active and the reactive power flow selectively in an electric transmission line. This paper focuses on the development of a novel algorithm capable of selecting the best combination of tap-settings for the compensating windings of the ST. Digital simulation model of the ST including a detailed tap-changer model has been developed in the PSCAD/EMTDC software package. The tap-changing algorithm of the ST has been implemented through a FORTRAN code that is interfaced with the rest of the model. Should there be any change in the magnitude of the compensating voltage and its phase angle, the tap-positions are readjusted accordingly. The results obtained from the simulation of the ST are compared with the simulation results of the UPFC which is a power electronics-based power-flow controller. The comparison shows good agreement between the results and hence validates the effectiveness of the proposed algorithm and the performance of the ST.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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