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Record W2929709030 · doi:10.1109/tec.2019.2907268

Saturable and Decoupled Constant-Parameter VBR Model for Six-Phase Synchronous Machines in State-Variable Simulation Programs

2019· article· en· W2929709030 on OpenAlexafffund
Navid Amiri, Seyyedmilad Ebrahimi, Juri Jatskevich, H.W. Dommel

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
KeywordsControl theory (sociology)Constant (computer programming)Variable (mathematics)Variable bitrateState variableState (computer science)Computer scienceSynchronous motorPhase (matter)Control engineeringMathematicsPhysicsEngineeringControl (management)Real-time computingAlgorithmArtificial intelligenceElectrical engineeringBit rateThermodynamics

Abstract

fetched live from OpenAlex

Six-phase electrical machines are often found in special purpose applications, such as vehicular, ship, and aircraft power systems, and are now becoming considered in renewable energy generation. For design and analysis of such power systems, accurate and numerically efficient models are required for various transient simulation programs. Recently, a constant-parameter voltage-behind-reactance (CPVBR) model has been developed for magnetically linear six-phase synchronous machines as an alternative to the conventional qd0 and VBR machine models. In this paper, a saturable CPVBR model is presented for six-phase machines, which includes the main flux saturation and achieves magnetically decoupled and constant RL interfacing branches. The new magnetically decoupled CPVBR (DCPVBR) model has many advantages for implementation in commonly available simulation programs where it can be easily interfaced with inductive and/or power-electronic circuit elements. The proposed DCPVBR model is demonstrated to have improved computational performance compared to the conventional qd0 and VBR 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 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 score0.715
Threshold uncertainty score0.928

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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