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Record W2994669962 · doi:10.1109/iecon.2019.8927209

Closed Loop Energy Balancing Control of Modular Multilevel Converters Under Capacitor Degradation

2019· article· en· W2994669962 on OpenAlexaff
Deepak Ronanki, Apoorva Kelkar, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCapacitorConvertersRippleCapacitanceDecoupling capacitorModular designSupercapacitorVoltagePower (physics)Computer scienceElectronic engineeringEngineeringTopology (electrical circuits)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

The modular multilevel converter (MMC) has emerged as a captivating multilevel converter topology for medium to high-power applications. Due to aging and chemical process, the impedance characteristics of the capacitor in the submodule (SM) changes. This can result in an increase of SM capacitor voltage ripple and unequal power distribution among the SMs in the arm. Furthermore, prolonged use of degraded capacitors could interrupt the normal operation of the MMC. Existing SM capacitor voltage balancing approaches for the MMC are based on an assumption that all SMs have equal capacitance and ignore the component degradation. Hence, there is a need for closed-loop balancing control techniques that use instantaneous monitoring of capacitor parameters to enhance the reliability of the MMC. This paper addresses this gap by introducing an energy-based balancing control of SM capacitors, which effectively balances and controls the energy distribution among the SMs in the arm under the capacitor degradation. For validation of the proposed control strategy, detailed simulation studies are carried out for half-bridge SM based MMC in PLECS software platform.

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 score0.880
Threshold uncertainty score0.370

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 head0.005
GPT teacher head0.173
Teacher spread0.168 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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