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Record W2119470727 · doi:10.1109/tie.2011.2165311

Current Control for an Indirect Matrix Converter With Filter Resonance Mitigation

2011· article· en· W2119470727 on OpenAlexaff
Marco Rivera, José Rodríguez, Bin Wu, José Espinoza, Christian A. Rojas

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2011
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsControl theory (sociology)Power factorTotal harmonic distortionFilter (signal processing)Low-pass filterAC powerActive filterEngineeringComputer sciencePower (physics)PhysicsControl (management)Voltage

Abstract

fetched live from OpenAlex

A predictive control scheme for the indirect matrix converter including a method to mitigate the resonance effect of the input filter is presented. A discrete-time model of the converter, the input filter, and the load is used to predict the behavior of the instantaneous input reactive power and the output currents for each valid switching state. The control scheme selects the state that minimizes the value of a cost function in order to generate input currents with unity power factor and output currents with a low error with respect to a reference. The active damping method is based on a virtual harmonic resistor which damps the filter resonance. This paper shows experimental results to demonstrate that the proposed control method can generate good tracking of the output-current references, achieve unity input displacement power factor, and reduce the input-current distortion caused by the input filter resonance.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.242
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

Citations142
Published2011
Admission routes1
Has abstractyes

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