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Record W2973370902 · doi:10.1109/tec.2019.2942828

Constant-Parameter Voltage-Behind-Reactance Modeling of Five-Phase Synchronous Machines With Air-Gap Flux Harmonics

2019· article· en· W2973370902 on OpenAlexafffund
Navid Amiri, Seyyedmilad Ebrahimi, Yingwei Huang, Juri Jatskevich, Steven D. Pekarek

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHarmonicsReactanceFlux (metallurgy)VoltageControl theory (sociology)Constant (computer programming)PhysicsHarmonic analysisAir gap (plumbing)Magnetic fluxMechanicsElectrical engineeringComputer scienceEngineeringElectronic engineeringMaterials scienceMagnetic field

Abstract

fetched live from OpenAlex

Five-phase electric machines possess several distinct features and are sometimes considered in vehicular power systems and various special-purpose energy sources. Design and analysis of such machine-converter systems are highly dependent on simulation software programs, where accurate and numerically efficient dynamic models of five-phase synchronous machines are essential. Depending on levels of fidelity and interfacing compatibility, the traditional models for five-phase machines include the coupled-circuit-phase-domain (CCPD) and qd0 models. To achieve a computationally-efficient model suitable for many simulation programs, in this paper, a new constant-parameter voltage-behind-reactance (CPVBR) model is proposed for five-phase synchronous machines where a simple interfacing circuit is achieved with constant RL branches. The proposed model is validated by experimental measurements and simulation studies. The proposed CPVBR model is demonstrated to have superior numerical efficiency and simulation speed compared to the existing conventional models.

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 categoriesMeta-epidemiology (narrow)
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 score0.705
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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.

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

Citations7
Published2019
Admission routes2
Has abstractyes

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