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Record W4411376409 · doi:10.1049/pel2.70070

Parallel Connected Converters with Interleaved Phase‐Shifted PWM Using a Common Carrier

2025· article· en· W4411376409 on OpenAlexaff
Wael Telmesani, Gregory J. Kish, John Salmon

Bibliographic record

VenueIET Power Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConvertersPulse-width modulationPhase (matter)Electronic engineeringThree-phaseComputer scienceControl theory (sociology)Electrical engineeringTopology (electrical circuits)PhysicsEngineeringVoltageControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT A novel interleaving approach for parallel‐connected dual three‐phase converters using a single unmodified carrier is introduced. This method simplifies implementation by adjusting the phase with an offset added to the reference signal, without altering pulse width modulation (PWM) timing. It effectively controls flux and circulating currents within coupled inductor cores. Conventional schemes face challenges such as high direct current (DC)‐offset in circulating currents, poor line‐voltage quality, and glitches in load currents. They also rely on multiple phase‐shifted carriers that require each leg to have its own carrier signal, adding synchronization complexity. While modern digital controllers can accommodate this, managing synchronization remains a challenge, especially for low‐cost controllers with limited PWM carrier generation. These limitations can restrict the number of parallel legs per phase, affecting scalability. The proposed approach overcomes these issues by using a shared carrier for all modulators and adjusting the reference signal to achieve the required phase shift, ensuring high‐quality line voltage and glitch‐free load currents while simplifying implementation. This approach enhances flexibility in phase shift and duty cycle control while eliminating DC‐offset in flux and circulating currents, reducing magnetic component size via a simple reference signal adjustment at zero‐crossing. Feasibility is validated across low and high frequencies through analysis, simulations, and experiments.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.245
Teacher spread0.237 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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