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Record W2565281529 · doi:10.1109/tec.2016.2639505

Constant-Parameter Voltage-Behind-Reactance Model of Six-Phase Synchronous Machines

2016· article· en· W2565281529 on OpenAlexaff
Navid Amiri, Seyyedmilad Ebrahimi, Mehrdad Chapariha, Juri Jatskevich, H.W. Dommel

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2016
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterfacingReactanceComputer scienceConvertersConstant (computer programming)Transient (computer programming)Control theory (sociology)VoltageControl engineeringEquivalent circuitElectronic engineeringThree-phaseElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Six-phase electrical machines have received significant attention in the literature due to their use in special purpose applications (e.g., aircraft, naval, and vehicular systems). Recently, such machines have also been considered for renewable energy systems including wind generators. Modeling of such machines in commonly available transient simulation programs is not straightforward, especially when the machine model is interfaced with external inductive network and/or power electronic converters. The available modeling approaches include the classical qd 0 model, the coupled-circuit-phase-domain and the voltage-behind-reactance (VBR) models (each having its interfacing challenges). This paper extends the prior research in this area and proposes a constant-parameter VBR model that has a very convenient constant RL-branch interfacing circuit (even for salient pole machines), which makes it simple to implement in most state-variable-based simulations programs. The presented computer studies demonstrate significant numerical advantages of the new model over the existing alternative 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.866

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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