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Modulation and Control Schemes of Modular Three Phase FCC-CSC for High Power Applications

2022· article· en· W4310521331 on OpenAlexaff
Li Ding, Cheng Xue, Nie Hou, Yunwei Li

Bibliographic record

Venue2022 IEEE Energy Conversion Congress and Exposition (ECCE) · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSpace vector modulationModular designModulation (music)Electronic engineeringInterleavingConvertersComputer scienceHarmonicVoltagePower (physics)Series and parallel circuitsElectrical engineeringPulse-width modulationEngineeringPhysics

Abstract

fetched live from OpenAlex

The AC-type flying capacitor (FC) technique instead of device series connection is a promising way to increase the system voltage rating for current source converter (CSC) based high-power applications. Improved DC-link voltage with low dv/dt and reduced common-mode voltage (CMV) can be achieved with proper modulation and FC voltage control. Parallel connection of power converters, on the other hand, is a popular choice to increase the system's current rating for higher power applications. Moreover, supervisor harmonic performance can be achieved through interleaving operation with reduced need for passive filters. Modular FCC-CSC structure can combine these merits to achieve a higher power rating, and better DC- and AC-side quality. More importantly, the improved AC quality can in turn help the regulation of FC voltage. This paper discussed the modulation and control schemes of modular three-phase FCC-CSC, the space vector modulation (SVM) based FC voltage control, and DC-link current balance strategies are proposed. Experiment results verified the effectiveness of the proposed methods.

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: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.770

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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