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Record W2555875666 · doi:10.1109/iecon.2014.7048634

A new H-bridge NNPC converter for 10kV class motor drives

2014· article· en· W2555875666 on OpenAlexaff
Mehdi Narimani, Bin Wu, Kai Tian, Zhongyuan Cheng, Navid R. Zargari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsRockwell Automation (Canada)Toronto Metropolitan University
Fundersnot available
KeywordsConvertersCapacitorController (irrigation)VoltageĆuk converterComputer scienceBuck–boost converterElectronic engineeringMATLABPower (physics)Boost converterEngineeringTopology (electrical circuits)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a new seven-level voltage source converter (VSC) for high-power medium-voltage (MV) motor drives. The proposed topology is composed of H-bridge connection of two nested neutral-point-clamped (NNPC) converters called 7L-HNNPC converter. This converter has interesting features such as operating over a wide-range of output voltages particularly for a range of (10kV-15kV) without the need for connecting power semiconductor in series, high quality output voltage, less number of components in compare to other classical seven-level topologies. A novel SPWM technique has been developed for the proposed 7L-HNNPC converter to control and balance of the flying capacitors. The outstanding property of the control strategy is that the control can be applied independently to each leg of the converter that reduces the complexity of the controller significantly. The performance of the proposed converter is studied under different operating conditions in the MATLAB/Simulink environment. The feasibility of the proposed converter is evaluated experimentally on a scale-down prototype.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.663

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)0.0010.000

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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 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

Citations6
Published2014
Admission routes1
Has abstractyes

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