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Record W2159168992 · doi:10.1109/tpwrd.2008.2002964

Rotary Power-Flow Controller for Dynamic Performance Evaluation—Part I: RPFC Modeling

2009· article· en· W2159168992 on OpenAlexafffund
Amadou Bâ, Tao Peng, S. Lefebvre

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2009
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsHydro-Québec
FundersPolytechnique Montréal
KeywordsTransformerElectromagnetic coilEngineeringQuadrature boosterControl theory (sociology)MATLABStatorElectrical engineeringElectrical impedanceVoltageElectronic engineeringDistribution transformerComputer science

Abstract

fetched live from OpenAlex

This paper (Part I) presents a model suitable for the performance analysis of a rotary power-flow controller (RPFC), which operates in a two-lines power system corridor. The RPFC consists of two transformers (one series and one shunt), and two rotary phase-shifting transformers operating in standstill. The shunt and series transformers and the two rotary phase-shifting transformers are represented by the conventional equivalent circuit of a two-winding transformer. The rotor windings of these rotary phase-shifting transformers are connected in parallel while their stator windings are in series. The resulting equipment is the RPFC whose macroscopic model is proposed. The model involves a series branch consisting of an adjustable voltage source (with its internal impedance) and a shunt branch consisting of a current source. The obtained RPFC model with the regulators, implemented in SPS/Matlab, is used for further simulations in Part II.

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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.015
GPT teacher head0.228
Teacher spread0.214 · 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

Citations33
Published2009
Admission routes2
Has abstractyes

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