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Record W2995217431 · doi:10.1109/iecon.2019.8927489

Algorithm for Improving Power Balance for Cascaded H-Bridge Multilevel under Staircase Modulation for Linear Loads

2019· article· en· W2995217431 on OpenAlexaff
Luccas M. Kunzler, Luiz A. C. Lopes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsModulation (music)ConvertersH bridgePower (physics)Computer scienceCapacitive sensingElectronic engineeringAmplifierCapacitorControl theory (sociology)Electrical engineeringEngineeringVoltagePulse-width modulationControl (management)CMOSPhysics

Abstract

fetched live from OpenAlex

This paper evaluates traditional modulation techniques for Cascaded H-Bridge Multilevel (CHBM) converter in terms of the power consumption by each cell. Under the scope of this study, this converter is connected in series with a Linear Power Amplifier (LPA) to build a Hybrid Power Amplifier (HPA), although it can be applied to any converter using staircase modulation. A new technique for improving the balance consumption by all the cells is proposed based on the selection of all possible combinations of the switching angles, generated by the Nearest Level of Control (NLC) technique. The novelty of this study relies on improving the balance, not only for resistive loads, but also for inductive and capacitive loads. By improving the power balance the cells will be designed in more uniform way, requiring smaller DC supplies and it makes it possible to use multi-output DC-DC converters on the cells input side. The new modulation technique will be detailed and simulations results will be presented to validate the technique.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.928

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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
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

Citations4
Published2019
Admission routes1
Has abstractyes

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