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Design of Three Limb Coupled Inductor using Cross-Coupled Windings to Produce Multi-Level Output Voltages and Reduced Magnetics

2021· article· en· W3186424533 on OpenAlexaff
Marius Takongmo, John Salmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorElectromagnetic coilVoltageRipplePulse-width modulationElectrical engineeringInverterEngineeringElectronic engineeringComputer sciencePhysicsControl theory (sociology)

Abstract

fetched live from OpenAlex

Coupled Inductor (CI) design for a 3-phase voltage source converter (VSC) with three parallel-connected 2-level inverter bridges in each phase is described. The CIs are desired to average the output of the parallel-connected inverter legs to produce 4-level phase and 7-level PWM line voltages with PWM voltage step smaller than the dc-link voltage. In addition, the frequency of the PWM line voltage is 6 times greater than the switching frequency. The system is useful for lowing filter size, weight and cost in mobile applications such as aerospace and electric vehicles where light-weight magnetic filters are desirable. The system is also useful to reduce cable interaction and common mode to ground noise in motor drives and uninterrupted power supplies. The CI design procedure is compared with the design of inductors used for current ripple cancellation. The weight of the CIs is shown to be 1/6thsmaller in comparison. Sample magnetic design of a 3-limb CI with metglas cores are presented for a 5.5 kW (300Vdc, 208V/15A) laboratory prototype operating with 60 Hz and 1.1 kHz fundamental voltages.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.087
GPT teacher head0.288
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2021
Admission routes1
Has abstractyes

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