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Record W2125971910 · doi:10.1109/iemdc.2009.5075285

Space-vector PWM for inverters with split-wound coupled inductors

2009· article· en· W2125971910 on OpenAlexaff
Behzad Vafakhah, John Salmon, Andrew M. Knight

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorWaveformInductancePulse-width modulationControl theory (sociology)VoltageTopology (electrical circuits)Modulation (music)Distortion (music)Coupling (piping)Electronic engineeringComputer scienceEngineeringPhysicsElectrical engineeringAcoustics

Abstract

fetched live from OpenAlex

This paper proposes a space-vector pulse width modulation (SVPWM) technique suitable for inverters with a coupled inductor output. The proposed approach is based on multi-level SVPWM and can offer superior performance when compared to other modulation techniques suitable for coupled inductor topologies. In particular the approach addresses issues related to common mode DC current balance in a three-limb inductor core and coupling between limbs in a three-limb core. The proposed approach provides a multi-level output voltage waveform while the total winding current distortion is significantly improved. This is achieved by appropriate choice of switching states such that effective low inductance modes are eliminated while generating half-wave symmetrical switching frequency voltage waveforms. The method presented is computationally simple and does not additional feedback signals when compared to conventional three-level SVPWM algorithms. The operation of the proposed drive structure is investigated by means of simulation results and verified by experimental results.

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: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.821

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 head0.206
Teacher spread0.192 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

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