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Dual-Input H-bridge Based 3-Phase Cascaded Multilevel Converter for Utility Scale Battery Applications

2021· article· en· W3216663697 on OpenAlexaff
Ahmed Sheir, Vijay K. Sood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDuty cycleBattery (electricity)Boost converterComputer scienceMATLABPower (physics)Ćuk converterH bridgeBuck–boost converterVoltageDual (grammatical number)Topology (electrical circuits)CapacitorConvertersElectronic engineeringElectrical engineeringPulse-width modulationEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a modified cascaded H-bridge CHB multilevel converter is introduced for utility-scale battery applications. The conventional H-bridge module, used as a building block in CHB, is replaced with a dual-input H-bridge module. In this, a boost converter is connected to a separate battery-set and integrated to each leg of the H-bridge. Such a configuration not only eliminates one power switch from each boost converter, but it also maintains the converter’s flexibility i.e. each boost converter can operate at its own duty cycle. The relationship between the boost duty cycle and modulation index is derived. The proposed topology is able to maintain uniform charging/discharging operation among all connected battery-sets while supplying/absorbing the desired reference power. The validity of the proposed converter is tested using a Matlab/Simulink model in a grid connected mode under different operating conditions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.275
Teacher spread0.240 · 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 designBench or experimental
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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