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Record W4406890669 · doi:10.1109/tpel.2025.3535238

A Zero Net Flux Modulation Scheme for 3-Leg Core Transformer Based 3-Phase AC–DC Topologies

2025· article· en· W4406890669 on OpenAlexaff
Sheron Bolonne, Gregory J. Kish, John Salmon

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNetwork topologyTopology (electrical circuits)TransformerThree-phaseControl theory (sociology)PhysicsElectrical engineeringElectronic engineeringComputer scienceVoltageEngineering

Abstract

fetched live from OpenAlex

Existing single-stage 3-phase ac–dc isolated topologies typically use three separate C-core high-frequency transformers (HFTs), which require significant space and reduce power density. A key challenge preventing the use of a more compact 3-leg core HFT is the lack of suitable modulation schemes, as current methods do not ensure a zero net flux condition considering all 3 legs. Violating this condition can lead to stray fluxes that induce common mode (CM) currents and high peak magnetizing currents, resulting in increased electromagnetic interferences and losses. This article proposes a new modulation scheme that, by strategic alignment of switching pulses for a dual inverter setup, both avoids CM winding voltage generation and preserves the shape of transformer secondary-side voltage waveforms for power transfer at twice the carrier frequency. These features allow the use of a single compact 3-leg core HFT. The scheme also offers: 1) high-quality 3/5-level ac pulse-width modulated grid voltages, 2) a 3-level high-frequency voltage for the power transmitted via transformer action, 3) higher overall average winding volt–seconds compared to dual active bridge converters at low modulation indices, and 4) reduced switching losses due to the discontinuous nature of the scheme. Adopting a 3-leg core can cut the size of 3 C-core designs by 35%–40% while achieving 4% total current harmonic distortion at full load. The presented modulation scheme is verified using both simulation and experimental results for a 250 V<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{{\text{dc}}}$</tex-math></inline-formula>, 122 V<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{\text{ac}}$</tex-math></inline-formula>, 750 W 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: none
Teacher disagreement score0.975
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 head0.018
GPT teacher head0.273
Teacher spread0.256 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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