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Record W2944465793 · doi:10.1109/pedes.2018.8707700

Device Loading of a Modular Multilevel Converter with Flying Capacitor Submodules

2018· article· en· W2944465793 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
KeywordsModular designConvertersCapacitorReliability (semiconductor)Power semiconductor deviceModularity (biology)Power electronicsElectronic engineeringComputer sciencePower (physics)Power moduleEngineeringElectrical engineeringVoltagePhysics

Abstract

fetched live from OpenAlex

Modular multilevel converters (MMCs) continuously capture attention in various industrial and electric transportation applications due to the advent features of modularity, improved harmonic performance and fault-tolerability. However, the operation of MMCs under stringent environmental conditions in some applications could lead to failure of power semiconductor devices, thereby imposing a reliability challenge in the MMC. Consequently, it is essential to analyze thermal behaviour of the power switching devices under different kinds of operating conditions and the control schemes for reliability-orientated converter design. In this paper, an electro-thermal model is developed in a PLECS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> simulation environment to calculate power loss and analyze thermal loading on semiconductor devices in flying capacitor submodules (FC-SM). The impact of an active circulating current control in the MMC with FC-SMs in terms of loss and thermal loading of power switching devices are holistically investigated in this paper. The methodology and the analysis are of immense importance and establishes the further study on reliability improvement of the MMC.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.312

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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