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Record W2143961968 · doi:10.1109/apec.2012.6165807

A discontinuous PWM scheme for lowering the switching frequency and losses in a 3-phase 6-switch 3/5-level PWM VSI using a 3-limb inductor

2012· article· en· W2143961968 on OpenAlexafffund
John Salmon, Jeffrey Ewanchuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsInductorPulse-width modulationRippleInverterElectromagnetic coilControl theory (sociology)Three-phaseInductancePhysicsEngineeringComputer scienceElectronic engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Standard interleaved 3-level pwm schemes used in multilevel 3-phase inverters such as the NPC are shown not to be suitable for multi-level inverters using a 3-limb coupled inductor. This paper illustrates how a standard discontinuous 3-level pwm scheme can be modified to provide an appropriate excitation for a 3/5 level 6-switch coupled inductor inverter (CII). The pwm reference signals and switching patterns are chosen to excite the inductor phase windings so that only 3 high permeability flux paths in the inductor core are used. As a result, the high frequency current ripple in the inductor windings are reduced together with the high frequency flux produced in the core. The resultant inverter operation can be operated at much lower switching frequencies (2-8 kHz), and levels that are more suitable for standard industrial applications. Experimental results illustrate that the new pwm scheme improves the efficiency of the inverter and lowers the power losses in the coupled inductor. The lower inductor losses also means that the physical size of the coupled inductor can be reduced. Experimental and simulated results are used to demonstrate the operation of the new pwm scheme using a variable speed inverter drive.

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 score0.958
Threshold uncertainty score0.845

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 communication0.0000.001
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 head0.064
GPT teacher head0.293
Teacher spread0.229 · 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

Citations11
Published2012
Admission routes2
Has abstractyes

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