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Record W4210811954 · doi:10.1109/tie.2022.3146539

Capacitor Pre-Charge Method for Back-to-Back Seven-Level Hybrid Clamped Converter Without Extra Power Supply

2022· article· en· W4210811954 on OpenAlexafffund
Hao Tian, Mingzhe Wu, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitorConvertersInrush currentResistorElectronic circuitElectrical engineeringOvervoltageVoltageElectronic engineeringComputer scienceEngineeringTransformer

Abstract

fetched live from OpenAlex

Multilevel converters with floating capacitors generally require precharging to build up the voltage on floating capacitors and dc-link before the normal operation. In particular, precharge is challenging for converters with overvoltage concerns under initial zero capacitor voltages. As a promising topology, seven-level hybrid clamped (7L-HC) converters also require precharge of capacitors and shall avoid high voltage stress during the precharge. Considering the high cost of deploying extra dedicated precharge circuits in a medium voltage system, the precharging method without relying on dedicated power supplies is favored. In this article, specific switching states and their corresponding selection schemes are designed to precharge floating capacitors of back-to-back 7L-HC converters. In this process, the inrush current and voltage stress can always be limited in the safe range. This method only needs series and shunt resistors typically already exist in practical converter circuits, resulting in low cost and ease of implementation. The design method of the resistances is also provided. Both simulation and experimental results validate the feasibility of this method.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.257
Teacher spread0.218 · 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.

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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

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