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Record W2910828419 · doi:10.1109/epec.2018.8598363

An Implementation of Cable Resistance in Modified Droop Control Method for Parallel-connected DC-DC Boost Converters

2018· article· en· W2910828419 on OpenAlexaff
Muamer M. Shebani, Tarirq Iqbal, John E. Quaicoe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVoltage droopConvertersMATLABVoltageComputer scienceCurrent (fluid)Electronic engineeringControl theory (sociology)Electrical engineeringVoltage sourceEngineeringControl (management)

Abstract

fetched live from OpenAlex

For load current sharing between parallel-connected DC-DC boost converters, a modified droop method with varying cable resistance is presented in this paper. The droop method provides a suitable current sharing in the system because there is no intercommunication link between the parallel-connected modules. However, when the cable resistance is considered, a small mismatch in output voltage of parallel-connected converters leads to circulating current and unequal load current sharing between two modules. To resolve this issue without using a communication link between modules, the modified droop method based on cable resistance implementation is proposed to improve the load current sharing. This can be established by adjusting the output voltage for each converter of the parallel-connected converters individually and according to modified load regulation characteristic of the droop method. The effectiveness of the proposed method is demonstrated using Matlab/Simulink simulations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.305
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2018
Admission routes1
Has abstractyes

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