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Record W1989067899 · doi:10.1109/tie.2013.2288194

Space-Vector-Based Synchronized Three-Level Discontinuous PWM for Medium-Voltage High-Power VSI

2013· article· en· W1989067899 on OpenAlexaff
Abdul R. Beig, Saikrishna Kanukollu, Khalifa Al Hosani, Apparao Dekka

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2013
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOvermodulationPulse-width modulationHarmonicsControl theory (sociology)InverterSynchronization (alternating current)Power (physics)Modulation (music)VoltageVoltage sourcePhysicsTopology (electrical circuits)Computer scienceElectronic engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The high-power voltage source inverters are switched at low frequency to reduce the losses in the inverter. The discontinuous pulsewidth-modulation (PWM) sequences reduce the switching frequency to two-thirds compared to the conventional continuous PWM sequences. At low switching frequency, the output will be rich in harmonics, and synchronization is a must to avoid subharmonics. This paper presents four basic types of synchronized discontinuous PWM (DPWM) sequences that ensure synchronization, three-phase symmetry, and half-wave symmetry. The proposed synchronized DPWM sequences are verified through simulation and experiment on a constant- v/f open-loop induction motor drive. The performance of the proposed DPWM algorithms is studied in the entire modulation region, including the overmodulation region, and is compared with that of the conventional synchronized space vector PWM. A different type of DPWM with low common-mode voltage is suggested for low-modulation region and verified experimentally.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 science0.0010.000
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 head0.026
GPT teacher head0.214
Teacher spread0.188 · 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

Citations117
Published2013
Admission routes1
Has abstractyes

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