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Record W4392693574 · doi:10.1109/icjece.2023.3313566

Optimal Busbar Design for the Press-Packed IGBT-Based Modular Multilevel Converter Submodule Considering Both Normal and Fault Ride-Through Conditions

2024· article· en· W4392693574 on OpenAlexvenueno aff
Wenju Sang, Wenyong Guo, Yang Cai, Wenming Guo, Chenyu Tian, Suhang Yu, Shaotao Dai

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBusbarModular designInsulated-gate bipolar transistorFault (geology)Computer scienceEngineeringElectrical engineeringElectronic engineeringReliability engineeringVoltageOperating system

Abstract

fetched live from OpenAlex

The performance of the power converter bus bar is not only determined by its normal operational design, but also related to its fault ride-through ability consideration. Conventional busbar design only takes the normal operational performance into account. This article proposes an optimal busbar design method for the modular multilevel converter (MMC) submodule, which takes both the normal and fault ride-through performance into account. The normal operational design is to realize low stray inductance and balanced inductance distribution between parallel capacitor branches. The basic structural design guideline for the MMC submodule is presented. Taking both the stray inductance and manufacturing cost into account, the optimal layout of the busbar is proposed. To balance the capacitor branch currents, the mathematical model of the busbar stray inductance is built. The influence of different busbar structures on the stray inductance is analyzed. The analysis is verified by simulation results. To improve the fault ride-through capability, special consideration is taken into account to reduce the thermal and mechanical stress at the weakest point. Simulation and experimental results verify the efficacy of the proposed approaches.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.559

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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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