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Record W2404015472 · doi:10.1109/tia.2016.2569400

The Effect of Two- and Three-Level Inverters on the Core Loss of a Synchronous Reluctance Machine (SynRM)

2016· article· en· W2404015472 on OpenAlexaff
Lesedi Masisi, Maged Ibrahim, John Wanjiku, Akrem Mohamed Aljehaimi, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersArthritis National Research Foundation
KeywordsMagnetic reluctanceControl theory (sociology)TorqueSynchronous motorCore (optical fiber)Reluctance motorComputer scienceEngineeringControl engineeringSwitched reluctance motorRotor (electric)Electrical engineeringControl (management)PhysicsTelecommunications

Abstract

fetched live from OpenAlex

The paper shows the reduction of core losses by using a three-level inverter over a two-level inverter for the same dc-bus voltage and switching frequency. A synchronous reluctance machine stator-core toroid was used in the analysis. Hence, the analysis is realistic as it accounts for mechanical effects through the stator core, and the distorted supply of the line-to-line inverter voltage supplies. The paper also shows the limitations of using finite element-derived excitation; hence, the toroid was directly supplied by the inverter. The reduction of core losses by use of a three-level inverter is significant at very high flux densities and frequencies; approximately 60% lower core losses. Therefore, it can reduce the cooling burden especially in the hard to cool teeth, increase the service life, and allow increased output. The latter is because the output of the three-level fundamental voltage is higher for the same dc-bus and switching frequency at lower machine 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.895
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.228
Teacher spread0.212 · 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 teacher head, 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

Citations27
Published2016
Admission routes1
Has abstractyes

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