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Multi-level Voltage Source Converters using Coupled Inductors and Parallel Connected Inverter Legs

2022· article· en· W4280653086 on OpenAlexaff
Marius Takongmo, Chenhui Zhang, John Salmon

Bibliographic record

Venue2022 IEEE Applied Power Electronics Conference and Exposition (APEC) · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorPulse-width modulationInverterVoltageConvertersElectromagnetic coilElectrical engineeringVoltage sourcePower (physics)Electronic engineeringThree-phaseControl theory (sociology)EngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

An Enhanced PWM scheme with a flux balancing technique is described for a 3-phase voltage source converter (VSC) with 4-parallel connected inverter legs per phase. The VSC produces high-quality 5-level phase and 9-level PWM line voltages for applications such as uninterrupted power supplies, aerospace, and electric vehicles. The voltage step of the PWM line voltage is four times smaller than the dc-link voltages, and the PWM frequency is 8 times greater than the switching frequency. These PWM output voltages can easily be filtered with a small ac inductor. The flux compensation is useful to reduce the peak flux, size, and weight of interphase coupled inductors when producing high-quality multi-level PWM outputs. Modular coupled inductors with cross-coupled windings on magnetic C-cores are used to connect the outputs of the parallel-connected inverter legs. This winding arrangement makes it possible to use standard magnetic cores rather than customized multi-limb coupled inductors. The modulation scheme described can easily be implemented in reduced digital hardware like the DSP. The feasibility of the PWM control with flux compensation is verified with simulations and experimental results of a low-power laboratory prototype.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.214
Teacher spread0.187 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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