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Record W2610845805 · doi:10.1109/pedes.2016.7914352

Self-excitation criteria of the synchronous reluctance generator in stand-alone mode of operation

2016· article· en· W2610845805 on OpenAlexaff
Sara Maroufian, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsExcitationControl theory (sociology)AccelerationResidualMagnetic reluctanceGenerator (circuit theory)Power (physics)Prime moverMagnetic fluxMode (computer interface)Switched reluctance motorElectric generatorElectric power systemComputer scienceRotor (electric)PhysicsEngineeringElectrical engineeringMagnetic fieldMagnetClassical mechanicsAlgorithm

Abstract

fetched live from OpenAlex

The self-excited synchronous reluctance generator represents a reliable alternative choice to supply electric power to remote communities or for emergency power applications. They are robust, simple, and less expensive compared to other types of brushless generators. However to trigger the self-excitation in stand-alone mode of operation certain criteria must be met. This paper studies the effect of these criteria on the self-excitation phenomenon and presents the minimum requirements to attain self-excitation. These criteria are the minimum required residual flux, and the maximum acceleration. The minimum residual flux in the magnetic core is determined using the machine's characteristic and the Energetic Model. It is also observed experimentally that there must be a constraint on the start-up acceleration of the prime mover which beyond that, self-excitation will not take place irrespective of the amount of residual flux in the system.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.213
Teacher spread0.208 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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