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Record W2333319883 · doi:10.1109/tie.2015.2511162

Multilevel Dodecagonal Voltage Space Vector Structure Generation for Open-End Winding IM Using a Single DC Source

2015· article· en· W2333319883 on OpenAlexaff
Mathews Boby, Sumit Pramanick, R. Sudharshan Kaarthik, S. Rahul, K. Gopakumar, L. Umanand

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2015
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsVoltageSpace (punctuation)PhysicsTopology (electrical circuits)Open-circuit voltageElectrical engineeringControl theory (sociology)Computer scienceEngineeringControl (management)

Abstract

fetched live from OpenAlex

In this paper, a new inverter topology is proposed for generating a multilevel dodecagonal voltage space vector structure for open-end winding induction motor (IM) using a single dc source. Dodecagonal voltage space vector structure eliminates fifth- and seventh-order harmonic voltages from phase voltage output and hence eliminates the fifth- and seventh-order harmonic currents through the motor leading to removal of the dominant sixth-order torque ripple generation in the motor over the entire modulation range. Multilevel space vector structure generates output voltage with better THD and low dv/dt. In the proposed topology, two three-level inverters drive an open-end winding IM, one inverter from each side. DC-link of primary inverter is from a dc source which delivers the entire active power, whereas the secondary inverter dc-link is maintained by a capacitor, which is self balanced during the inverter operation. The PWM scheme implemented ensures low switching frequency for primary inverter. Secondary inverter operates at a small dc-link voltage. Hence, switching losses are small for both primary and secondary inverters. Experimental results show the elimination of fifth- and seventh-order harmonic voltages over the entire modulation range, validating effectiveness of the proposed multilevel inverter scheme.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.120
GPT teacher head0.274
Teacher spread0.155 · 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.

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

Citations27
Published2015
Admission routes1
Has abstractyes

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