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Record W2155994771 · doi:10.1109/08ias.2008.192

Performance of a High Speed Motor Drive System Using a Novel Multi-Level Inverter Topology

2008· article· en· W2155994771 on OpenAlexaff
Jeffrey Ewanchuk, John Salmon, Andrew M. Knight

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInverterTopology (electrical circuits)Induction motorVoltageControl theory (sociology)Reduction (mathematics)HarmonicNetwork topologyRotor (electric)Voltage dropHarmonic analysisComputer scienceElectronic engineeringEngineeringElectrical engineeringMathematicsPhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper examines the performance of a novel multilevel six-switch three phase inverter drive for low-voltage high speed motor applications. Hard switched voltage source inverter topologies are limited in terms of switching frequency and therefore as the frequency modulation ratio decreases, the resulting increase in harmonic currents may cause excessive rotor heating or lead to larger than desired output reactors with a series voltage drop. The multi-level inverter structure offers an increased number of voltage levels, reduced dead-time effects and a significant reduction in harmonic content, resulting in a reduction of total losses compared to the standard six-switch three phase inverter. The harmonic reduction provided by the multilevel topology relative to the standard inverter is experimentally demonstrated with a 15 HP 18,000 rpm induction machine.

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: Empirical
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.058
GPT teacher head0.222
Teacher spread0.164 · 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

Citations18
Published2008
Admission routes1
Has abstractyes

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