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Record W1951184406 · doi:10.24908/pceea.v0i0.5881

NON-LINEAR MODEL OF A SYNCHRONOUS GENERATOR FOR DYNAMIC SIMULATIONS, TRAINING AND TEACHING

2015· article· en· W1951184406 on OpenAlexaffvenue
Tsotie Wamba Juste, Gabriel Ekemb, R. Wamkeue

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsComputer scienceMATLABControl engineeringGenerator (circuit theory)Nonlinear systemField (mathematics)HydropowerPermanent magnet synchronous generatorHydroelectricityElectric power systemPower (physics)SimulationControl theory (sociology)VoltageEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The development of digital and simulation tools is a major challenge for the teaching of electro-mechanics and related fields, as their suitability depends both on scientific and educational requirements. In this regard, this paper presents a synchronous generator model, developed for the teaching and training in the hydroelectric field. The model developed with MATLAB/SIMULINK and having the characteristics of being dynamic, versatile, flexible and easily integrated in most electrical power generation system using synchronous generator, is made of a mechanical part, an electrical part, and an excitation circuit. It is a dynamic nonlinear model which can perform most of the simulations of real life situations by acting on the load and presenting the resulting curves. Moreover, it allows direct simulation without the need for initial conditions re-evaluation. In this paper, its stepwise system development is covered alongside its subsystems. Our results indicate a successful application of the developed model in a complete hydropower system production, and suggest the importance of such tool.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.226
Teacher spread0.212 · 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
Published2015
Admission routes2
Has abstractyes

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