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Cascaded H-Bridge Converter-Based PMSM Drive: PS-PWM and LS-PWM Modulation Comparison

2025· article· W4415968856 on OpenAlexaff
Paul Inuwa Adamu, Daniel Legrand Mon Nzongo, Chunyan Lai, Animesh Anik, K. Lakshmi Varaha Iyer

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMagna International (Canada)Concordia University
Fundersnot available
KeywordsTotal harmonic distortionPulse-width modulationConvertersControl theory (sociology)VoltageHarmonicModulation (music)Distortion (music)

Abstract

fetched live from OpenAlex

Multilevel converters (MCs) are popular due to their higher voltage levels, low dv/dt, low harmonics, and high-power handling capabilities. This paper evaluates the use of two pulse width modulation (PWM) schemes for MCs: Phase-Shifted PWM (PS-PWM) and Level-Shifted PWM (LS-PWM). At first, the Cascaded H-Bridge (CHB) converter is utilized to demonstrate the principles and switching patterns of PS-PWM and LS-PWM. Thereafter, a 23 level CHB converter is designed for a permanent magnet synchronous machine (PMSM) drive application as a case study. The switching and conduction losses in different switches and submodules are analyzed in details under different PMSM operation points. The total harmonic distortion (THD) and efficiency of the converter are also compared. The simulation results show that PS-PWM performs better owing to reduced THD in the voltage and current as well as equal loss distribution, compared to LS-PWM. However, PS-PWM contributes to higher switching losses.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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 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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