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Record W2568398003 · doi:10.1109/iecon.2016.7793759

Optimal low-switching frequency pulsewidth modulation of dual modular multilevel converter for medium-voltage open-end stator winding induction motor drive

2016· article· en· W2568398003 on OpenAlexaff
Amarendra Edpuganti, Akshay Kumar Rathore, Bhakti M. Joshi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsConcordia University
Fundersnot available
KeywordsTopology (electrical circuits)StatorModular designWaveformPulse-width modulationInduction motorCapacitorComputer scienceVoltageModulation (music)Motor driveControl theory (sociology)Electronic engineeringEngineeringElectrical engineeringPhysicsControl (management)Acoustics

Abstract

fetched live from OpenAlex

The first dual multilevel converter (MLC) topology for medium-voltage (MV) open-end stator winding induction motor (OESW-IM) drives was proposed in 1990s. Till now, MLC topologies to generate three-level, five-level, seven-level, and nine-level voltage waveforms for OESW-IM drives have been proposed. This paper proposes a new topology for OESW-IM drives based on modular multilevel converter (MMC) topology. The control requirements of the proposed topology are as follows: balancing floating capacitor voltages, removal of common-mode current components in stator current, and low-switching frequency operation with nearly sinusoidal machine stator currents. In this paper, synchronous optimal pulsewidth modulation (SOP) has been improved to satisfy the control requirements of the proposed topology. The proposed method has been validated using a low power experimental setup of dual three-level MMC.

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 score0.760
Threshold uncertainty score0.714

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 communication0.0000.001
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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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