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A Cascaded Multilevel Topology with Optimized Modulation for Achieving Zero-Voltage Switching in High-Blocking-Voltage Devices

2025· article· en· W4413146034 on OpenAlexaff
Javad Ebrahimi, Shima Shahnooshi, Suzan Eren, Alireza Bakhshai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsBlocking (statistics)Topology (electrical circuits)Modulation (music)VoltageElectronic engineeringComputer scienceMaterials scienceElectrical engineeringPhysicsEngineeringComputer network

Abstract

fetched live from OpenAlex

This paper proposes a novel multilevel inverter topology that integrates a cascaded half-bridge modules with multiple full-bridge modules. A key innovation of the proposed structure is its ability to ensure Zero Voltage Switching (ZVS) for high-voltage switching devices within the full-bridge modules, effectively reducing switching losses and enabling the use of seriesconnected devices to meet high blocking voltage requirements. To fully leverage the modular architecture of the inverter, a phaseshifted carrier-based modulation strategy is adopted. This method utilizes a single, modified reference signal for all half-bridge cells, which is compared against phase-shifted carrier signals to generate a high-frequency stepped waveform. The waveform is then unfolded by the full-bridge modules to achieve the desired multilevel output with ZVS operation. The proposed topology is scalable and flexible, allowing easy adjustment of the number of half-bridge and full-bridge units to meet diverse application needs. The validity and effectiveness of the design and modulation scheme are demonstrated through detailed simulation results of a 25-level single-phase inverter, confirming its suitability for highefficiency, high-voltage applications.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.236
Teacher spread0.225 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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