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Record W2807652180 · doi:10.1109/cpe.2018.8372504

Generalized SVPWM-based capacitor voltage balancing for modular multilevel converters

2018· article· en· W2807652180 on OpenAlexaff
Deepak Ronanki, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPulse-width modulationConvertersCapacitorModular designControl theory (sociology)VoltageModulation (music)Topology (electrical circuits)Electronic engineeringEngineeringComputer sciencePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Submodule (SM) capacitor voltage and arm voltage balancing are imperative for the reliable operation of modular multilevel converters (MMCs). In this paper, a new, simple and generalized discontinuous space-vector pulse width modulation (PWM)-based SM capacitor voltage balancing approach is proposed and developed within the carrier-based PWM for MMCs. Discontinuous modulation can be achieved by addition of zero-sequence component to the PWM modulator so that MMC arm is clamped to positive or negative dc-bus during a particular interval. In addition, a simple generalized approach for determination of zero-sequence component using the zero vector distribution factor is proposed. The dynamic performance of the proposed generalized discontinuous modulation technique and voltage balancing approach for half-bridge (HB)-SM based 5-level MMC is demonstrated through simulation results in PLECS platform. The proposed scheme is applicable to any number of SMs, any SM type and balanced or unbalanced phase voltages.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.224
Teacher spread0.210 · 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 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

Citations10
Published2018
Admission routes1
Has abstractyes

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