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Record W1967034281 · doi:10.1109/tec.2014.2385099

Constant-Parameter Circuit-Based Models of Synchronous Machines

2015· article· en· W1967034281 on OpenAlexafffund
Mehrdad Chapariha, Francis Therrien, Juri Jatskevich, H.W. Dommel

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2015
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStatorControl theory (sociology)InterfacingRotor (electric)ReactanceConstant (computer programming)Computer scienceElectromagnetic coilEquivalent circuitSynchronous motorTransient (computer programming)DamperControl engineeringVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Representation of synchronous machines using constant-parameter voltage-behind-reactance (VBR) formulations improves accuracy and numerical efficiency of power systems transient simulation programs. This paper extends the VBR representation to the rotor circuit and presents two new formulations that achieve direct constant-parameter interfacing of the rotor and stator terminals with arbitrary external networks. In the first model, the entire machine is represented by constant RL branches that have algebraic coupling among the circuit variables. In the second model, all damper windings are implemented in state-space form to increase the numerical efficiency, while the stator and field windings are provided as constant-parameter circuits. The proposed models are validated against the commonly used and some state-of-the-art alternative models using a single machine with a simplified ac excitation system. Computer studies demonstrate the improved accuracy and efficiency of the proposed models when external rotor circuitry is considered.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.206
Teacher spread0.186 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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