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Record W2124357188 · doi:10.1109/tpel.2013.2280723

Improved Method for Paralleling Reduced Switch VSI Modules: Harmonic Content and Circulating Current

2014· article· en· W2124357188 on OpenAlexaff
Mehdi Narimani, Gerry Moschopoulos

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2014
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsWestern University
Fundersnot available
KeywordsHarmonicsPulse-width modulationInverterHarmonicReduction (mathematics)Harmonic analysisVoltageElectronic engineeringCurrent (fluid)Three-phaseModulation (music)Computer scienceSequence (biology)Control theory (sociology)Topology (electrical circuits)EngineeringMathematicsElectrical engineeringPhysicsAcousticsControl (management)Chemistry

Abstract

fetched live from OpenAlex

A new zero-sequence circulating current (ZSCC) reduction method for multimodule voltage source inverters (MVSIs) consisting of P three-phase inverters that are connected in parallel is proposed in this paper. Four-switch inverter modules are used instead of the conventional six-switch modules to reduce the cost. In this paper, the effectiveness of the proposed ZSCC reduction method is studied using selective harmonic elimination pulse width modulation. The proposed ZSCC reduction method can remove about P times the number of harmonics using the same switching frequency as compared to more conventional methods, as explained later in this paper. The concepts discussed in the paper are confirmed with results obtained from an MVSI experimental 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 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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.258
Teacher spread0.232 · 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

Citations51
Published2014
Admission routes1
Has abstractyes

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