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2DoF BTSPWM for Parallel Current Source Converter with Improved CMV and Harmonic Performance

2022· article· en· W4310475753 on OpenAlexaff
Li Ding, Cheng Xue, Pengcheng Liu, 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
KeywordsInterleavingHarmonicElectronic engineeringInverterConvertersPulse-width modulationRectifier (neural networks)Computer scienceHarmonic analysisPower (physics)VoltageEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The three-phase current source converter (CSC) can work as a buck-type rectifier or boost-type inverter with a wide voltage regulation range, which is a promising solution for single-stage power conversion. Parallel connection of power converters is a popular choice to increase the power rating and system reliability. Meanwhile, supervisor harmonic performance can be achieved through interleaving operation. Carrier-based sinusoidal pulse width modulation (SPWM) is widely adopted for interleaved converters due to its easy and modular implementation features. In this paper, a 2 degree-of-freedom (2DoF) optimization-based bi-tri logic SPWM (BTSPWM) is proposed for a parallel CSC system, where the harmonic spectrum of output and common-mode voltage (CMV) is analyzed comprehensively. The carrier shift angles are optimized to improve the CMV and harmonic performance for interleaving BTSPWMs. Meanwhile, the optimized zero-state replacement (ZSR) method can be combined with 2DoF modulations to further reduce the CMV. The effectiveness of the proposed method is verified on a 2-CSC parallel system with experiment results.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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 designOther design
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

Citations2
Published2022
Admission routes1
Has abstractyes

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