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Record W2538936231 · doi:10.1109/iecon.2008.4758015

Analytical calculation of current and voltage stresses of Two-Stage Matrix Converter’s power semiconductors

2008· article· en· W2538936231 on OpenAlexafffund
Mahmoud Hamouda, Farhat Fnaiech, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDisplacement (psychology)VoltageDisplacement currentPower factorSemiconductorPower (physics)LaggingSemiconductor deviceConvertersMatrix (chemical analysis)MechanicsPhysicsMathematicsElectrical engineeringMaterials scienceEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The currentpsilas path through the power semiconductors of matrix converters depends inherently on the load side displacement factor. In principle, two particular cases should be investigated. They correspond to a lagging/leading output displacement angle lower and superior to pi/6 respectively. This paper determines new analytical expressions of the current and voltage stresses of two-stage matrix converterpsilas power semiconductors for a load side lagging/leading displacement angle less than pi/6. The first novelty in the proposed approach is that any assumption which decouples the two stages is adopted. The second one is that the proposed formulas are not restricted to the case of unity input displacement factor i.e. they are available for a variable IDF operation. Besides the well known influence of the input displacement factor on the reactive power demanded from the utility and the maximum achievable voltage transfer ratio, it is shown that this factor also affects the current stress on the power semiconductors. A comparative study carried out between the results of analytical computations and those of numerical simulations shows that the two methods agree well and emphasizing thus the effectiveness and accuracy of the proposed analytical expressions.

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 score0.933
Threshold uncertainty score0.456

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 head0.033
GPT teacher head0.283
Teacher spread0.251 · 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

Citations2
Published2008
Admission routes2
Has abstractyes

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