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Record W2290729535 · doi:10.14288/1.0065143

Phase-domain synchronous generator model for transients simulation

2009· article· en· W2290729535 on OpenAlexaff
K.W. Louie

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPermanent magnet synchronous generatorPhase (matter)Computer scienceGenerator (circuit theory)Control theory (sociology)PhysicsElectrical engineeringPower (physics)VoltageEngineeringControl (management)

Abstract

fetched live from OpenAlex

Three-phase synchronous generators which convert mechanical energy into electrical energy are the main power sources of power systems. In this thesis a phase-domain synchronous generator model which is developed directly in the stator reference frame is presented. The electrical and mechanical parts of a three-phase synchronous generator are separately modeled by two different, sets of differential equations which are discretized with the trapezoidal rule of integration. These two parts are linked together by the rotor position angle and the electromagnetic torque generated by the interactions of the different magnetic fields, resulting in a linear generator model. In order to represent the generator more accurately, the linear generator model is modified to include magnetic saturation effects, resulting in a non-linear generator model. A formula is developed to account for the saturation as a function of the rotor angle and the total magnetomotive force angle. The machine parameters in the stator reference frame are obtained from the given characteristic quantities by a suitable data conversion scheme. To verify the linear generator model, a comparison between the simulation results of a three-phase short-circuit at the terminals of the machine obtained with the new linear model and the model in the EMTP is conducted.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.542

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 head0.193
Teacher spread0.183 · 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 teacher head, 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

Citations1
Published2009
Admission routes1
Has abstractyes

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