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Record W2965134523 · doi:10.1109/compel.2019.8769665

Analysis of Submodule Capacitor Voltage Ripple and Second-Harmonic Current in MMCs

2019· article· en· W2965134523 on OpenAlexaff
Xianghua Shi, Shaahin Filizadeh, Liwei Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Manitoba
Fundersnot available
KeywordsRippleCapacitorHarmonicsConvertersTopology (electrical circuits)VoltageHarmonic analysisModular designHarmonicRedundancy (engineering)Electronic engineeringComputer sciencePhysicsElectrical engineeringControl theory (sociology)EngineeringAcoustics

Abstract

fetched live from OpenAlex

This paper presents a straightforward approach to analyze the 2ndharmonic currents in modular multilevel converters. The submodule capacitor voltage ripple is derived based upon the capacitor's charge variations instead of commonly used energy variations, resulting in simplified calculations and an explicit expression for the voltage ripple to calculate the ripple value and select a proper SM capacitor size. Using analysis of the dc-side loop, a closed-form expression for 2ndharmonics in the arm currents is obtained considering SM redundancy. To validate the theoretical analyses, a 101-level, 500-MW half-bridge MMC is simulated in PSCAD/EMTDC. Experimental results of a downscaled laboratory setup are also presented. Comprehensive comparisons of the theoretical results obtained by the proposed method against simulation and experimental results as well as against an existing method demonstrate its superior accuracy.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.206
Teacher spread0.200 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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