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Record W2809080720 · doi:10.1109/tia.2018.2849407

The Loss of Self-Excitation Capability in Stand-Alone Synchronous Reluctance Generators

2018· article· en· W2809080720 on OpenAlexafffund
Maged Ibrahim, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersConcordia University
KeywordsFlux linkageMagnetic reluctanceRotor (electric)ExcitationGenerator (circuit theory)Control theory (sociology)Permanent magnet synchronous generatorMagnetic fluxResidualEngineeringTorqueElectromagnetic coilSynchronous motorAutomotive engineeringMagnetMechanical engineeringComputer scienceDirect torque controlPower (physics)VoltageElectrical engineeringMagnetic fieldPhysicsInduction motor

Abstract

fetched live from OpenAlex

This paper analyzes the capability of synchronous reluctance generators (SynRGs) to achieve successful self-excitation when operating in stand-alone systems. To initiate the self-excitation process, the machine steel core has to retain sufficient residual flux during the generator startup. This residual flux depends on the magnetic properties of the machine core as well as the previous machine operating condition. A hysteresis model is developed to simulate the flux linkage-current characteristics of the SynRG for different operating conditions. The model then calculates the machine residual flux linkage after each operation, which indicates the machine capability for self-excitation during the next startup. The simulation results show that the rotor steel core of the synchronous reluctance machine (SynRM) can be demagnetized when the generator is subjected to a short-circuit condition or when the generator shaft is subjected to abrupt deceleration. These simulation results are also validated by experimental measurements on a 5 hp SynRM.

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: none
Teacher disagreement score0.696
Threshold uncertainty score0.441

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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