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Analysis of the Isolated Modular Multilevel DC-DC Converter by Considering the Impact of Magnetizing Inductance

2020· article· en· W3102652900 on OpenAlexaff
Mahmoud Mehrabankhomartash, Amirnaser Yazdani, Deepak Divan, Maryam Saeedifard

Bibliographic record

VenueIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society · 2020
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsToronto Metropolitan University
FundersNational Science Foundation
KeywordsConvertersInductanceModular designTransformerForward converterElectronic engineeringFlyback converterComputer scienceĆuk converterElectrical engineeringMATLABVoltageEngineeringBoost converter

Abstract

fetched live from OpenAlex

Interconnection of Medium Voltage DC (MVDC) grids necessitates reliable and efficient DC-DC converters. Among the existing topologies, the Isolated Modular Multilevel DC-DC (IMM DC-DC) converter is a promising solution. In the technical literature, the impact of the magnetizing inductance of the High Frequency Transformer (HFT) on the operation and control of the converter is neglected. This paper analyzes the IMM DC-DC converter by focusing on the impact of the magnetizing inductance on the operation and control of the converter. Power transfer capability, arm current, reactive power, and soft-switching are discussed by considering the impact of the magnetizing inductance. This paper demonstrates that the magnetizing inductance of the HFT is a key parameter that needs to be carefully selected, as it can improve soft-switching operation, while reducing the power transfer capability of the converter. Simulation studies of a test system in the Matlab/Simulink software environment verify the presented modeling and analysis of the IMM DC-DC converter.

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)0.001
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: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.238
Teacher spread0.206 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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Same venueIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics SocietySame topicHVDC Systems and Fault ProtectionFrench-language works237,207