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Record W2947429490 · doi:10.1109/apec.2019.8722129

DC Voltage Control Architecture in Renewable Energy Based Three-Level Converters

2019· article· en· W2947429490 on OpenAlexaff
Emanuel Serban, Cosmin Pondiche, Helmine Serban, Cristian Lascu, Octavian Cornea

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsSimon Fraser UniversitySchneider Electric (Canada)
Fundersnot available
KeywordsInrush currentConvertersComputer scienceElectrical engineeringPhotovoltaic systemCapacitorBuck converterVoltageElectronic engineeringEngineeringTransformer

Abstract

fetched live from OpenAlex

The three-level converters with series connected capacitors need to address the dc voltage control and balancing under any conditions, whether in operational or standby. The effect of inrush transients in dc bus results in degradation of components lifetime, resulting in premature failures. To address these issues, we present a new power conversion architecture to economically simplify the subsystems, with the following integrated functionalities: auxiliary dc power supply, lossless dc split-bus balancing and inrush current elimination. These integrated functions are achieved by controlling a bi-directional dc-dc three-level converter topology. The three-level buck-boost (3L-BB) interface converter provides the capability to supply power to various auxiliary loads for the main three-phase three- level converter, while performing dc voltage regulation and balancing. The system initialization feature that precharges the dc split-bus capacitors through 3L-BB eliminates the inrush current, allowing the use of components with longer lifetime at lower cost. The proposed system architecture was evaluated within a 100kW three-phase three-level grid- connected photovoltaic (PV) inverter. The evaluation results demonstrate the architecture's performance for power supply capability with voltage regulation, lossless dc splitbus balancing and inrush current elimination.

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 categoriesInsufficient payload (model declined to judge)
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 score0.975
Threshold uncertainty score0.999

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)0.0020.000

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 head0.009
GPT teacher head0.174
Teacher spread0.165 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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