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Record W2907279452 · doi:10.1109/iecon.2018.8592847

Pseudo Derivative Feedback Circulating Current Suppression Controller for Modular Multilevel Converter with Flying Capacitor Submodules

2018· article· en· W2907279452 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
KeywordsRippleModular designCapacitorControl theory (sociology)Controller (irrigation)ConvertersComputer scienceElectronic engineeringVoltageHarmonicTotal harmonic distortionEngineeringElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

The modular multilevel converter (MMC) employing multilevel submodules (SMs) displeys a reduced footprint size, voltage ripple and an improved efficiency compared to standard SMs. Due to capacitor voltage fluctuations in the SMs of the MMC, circulating current (CC) flows among the phase-legs. These currents can affect the system efficiency and menace the safe operation of MMC. Therefore, an active closed-loop CC controller is imperative for reliable operation of the MMC. The classical methods exhibit an awful steady-state performance, limited harmonic filtering, instability during load variations and complexity in digital implementations. This paper introduces a pseudo-derivative-feedback (PDF) based CC suppression control of the MMC in synchronous dq-frame. The design and implementation of the PDF controller are presented and adapted to minimize the CC. The theoretical analysis and numerical results obtained through simulations in PLECS software platform for seven-level flying capacitor SM based three-phase MMC are presented to verify the advantages of the PDF-based CC control.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.965
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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 teacher head, 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

Citations2
Published2018
Admission routes1
Has abstractyes

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