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ZPUC9-MMC: An Increased Voltage Level Modular Multilevel Converter

2021· article· en· W3176854170 on OpenAlexaff
Saeed Arazm, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsTotal harmonic distortionWaveformConvertersVoltageModular designCapacitorElectronic engineeringTransient (computer programming)MATLABVoltage sourceComputer scienceControl theory (sociology)EngineeringTopology (electrical circuits)Electrical engineering

Abstract

fetched live from OpenAlex

In this paper a nine level Z-packed U-cell (ZPUC9) multilevel converter has been proposed as a submodule of modular multilevel converter (MMC) to increase the voltage level at the output waveform. Single DC source ZPUC9-MMC generates 17-level phase voltage and 33-Level line voltage waveform through single submodule per arm which increases the voltage quality owing to total harmonic distortion (THD) reduction and increase the reliability due to reduced counts of devices. Single DC source operation for single-phase and three-phase operation as well as the modularity is the main advantages of proposed configuration in comparison with the counterpart converters. Voltage balancing integrated with modulation technique has been used to control and regulate the flying capacitors (FCs) voltages of the converter. Simulation results obtained by MATLAB-Simulink validate the performance of proposed converter in transient and steady state in stand-alone mode.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.226
Teacher spread0.198 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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