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Record W4206564846 · doi:10.1109/tpel.2021.3136566

Coupled Inductors for High-Frequency Drives With Parallel-Connected Inverter Legs

2021· article· en· W4206564846 on OpenAlexafffund
Marius Takongmo, Chenhui Zhang, John Salmon

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInductanceInductorLeakage inductanceElectromagnetic coilInverterVoltageElectrical engineeringEquivalent series inductanceConvertersSeries and parallel circuitsInductive couplingEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

In this article, coupled inductors with cross-coupled windings are described and designed for voltage source converters with parallel-connected inverter legs in each phase. The nature of the cross-coupled windings produces a very low effective series output inductance, related to the intralimb leakage inductance rather than the higher interlimb leakage. The effective inductance between the parallel inverter output terminals can be made high to reduce circulating currents and is related to inductor magnetizing inductance. These two features make the use of parallel inverters attractive for generating multilevel high-frequency fundamental pulsewidth modulation output voltages with a very low fundamental voltage drop across the coupled inductors. Sample magnetic designs are presented for a three-phase system using three inverter legs connected in parallel in each phase. The effective output inductance of the cross-coupled winding arrangement is compared with that of the coupled windings on separate limbs without cross-coupling and is shown to be 97% smaller. The performance of the two inductor winding arrangements is demonstrated with simulation and experimental results of an 11 kW (300 Vdc, 208 Vac/30 A) laboratory prototype operating with fundamental frequencies in the kilohertz range up to 5 kHz.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.200
Teacher spread0.191 · 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.

Study designBench or experimental
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
Published2021
Admission routes2
Has abstractyes

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