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Record W3096356875 · doi:10.1109/jestpe.2020.3031206

A Robust Multivariable Approach for Current Control of Voltage-Source Converters in Synchronous Frame

2020· article· en· W3096356875 on OpenAlexafffund
Masoud Karimi-Ghartemani, Houshang Karimi

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsControl theory (sociology)Multivariable calculusVoltage sourceConvertersReference frameController (irrigation)Computer scienceMIMOGridFrame (networking)Control engineeringRobust controlVoltageControl systemElectronic engineeringEngineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

This article presents a new vector current controller for the grid-connected three-phase voltage-source converter (VSC). The VSC is increasingly used in many applications, such as distributed generation, motor drives, and high-voltage dc (HVdc) systems. From a control system perspective, this presents a two-input two-output coupled problem when transformed into the synchronous rotating reference frame. The common approach is to decouple the two control loops and use two single-input single-output (SISO) controllers. There are also some more recent approaches to treat the problem using multivariable or model predictive techniques. The proposed controller of this article is based on a multi-input multi-output (MIMO) approach that, compared with existing approaches, offers: 1) a significantly improved robust performance and 2) a convenient systematic and optimal design procedure. Details of the proposed approach and multiple simulation and experimental testings are presented to demonstrate the proposed controller.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

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Same venueIEEE Journal of Emerging and Selected Topics in Power ElectronicsSame topicMicrogrid Control and OptimizationFrench-language works237,207