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Record W2141091007 · doi:10.1109/tec.2007.914400

Saturation Modeling and Stability Analysis of Synchronous Reluctance Generator

2008· article· en· W2141091007 on OpenAlexaff
Souvik Guha, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2008
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsControl theory (sociology)Saturation (graph theory)Magnetic reluctancePermanent magnet synchronous generatorGenerator (circuit theory)Transient (computer programming)Stability (learning theory)Steady state (chemistry)Electric power systemPower (physics)MathematicsComputer scienceEngineeringPhysicsMagnetElectrical engineeringThermodynamicsChemistry

Abstract

fetched live from OpenAlex

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A new method of representing magnetic saturation in synchronous reluctance generator has been proposed in this paper. A linearized model of synchronous reluctance generator has been developed applying the proposed saturation model to perform the steady-state stability analysis. The effect of <formula formulatype="inline"><tex>$d$</tex> </formula>- and <formula formulatype="inline"><tex>$q$</tex></formula>-axis saturation on the steady-state stability of a synchronous reluctance generator has been investigated using the proposed linearized machine model. Effects of different loading conditions such as active power, reactive power, and power factor on the steady-state stability have also been looked into. Moreover, the effect of <formula formulatype="inline"><tex>$d$</tex></formula>- and <formula formulatype="inline"><tex>$q$</tex></formula>-axis saturation on the transient stability analysis has been investigated in the case of a three-phase symmetrical ground fault at the machine terminals. </para>

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.567
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.184
Teacher spread0.173 · 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

Citations28
Published2008
Admission routes1
Has abstractyes

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