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A Single-Carrier PWM Method for Uniform Step Asymmetrical Multilevel Converters

2023· article· en· W4390416995 on OpenAlexaff
P. M. Lingom, Joseph Song‐Manguelle, Joselyn Stephane Menye, Roland Unruh, Mamadou Lamine Doumbia, Tao Jin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsPulse-width modulationConvertersVoltageModulation (music)Topology (electrical circuits)Computer scienceElectronic engineeringMicrocontrollerCapacitorControl theory (sociology)Scheme (mathematics)Electrical engineeringMathematicsEngineeringPhysicsComputer hardware

Abstract

fetched live from OpenAlex

This paper proposes a general design principle of a single-carrier-based pulse width modulation (SC-PWM) technique for a uniform step, asymmetrical multilevel converter with k series-connected H-bridge inverters under unequal DC-link voltages within each phase. The proposed modulation approach is a straightforward implementation of the conventional phase-disposition PWM. It may produce many output voltage levels (any odd number between 2k+1 and 3<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup>) using only one carrier signal without complicating its implementation scheme. This makes it suitable for real-time implementation in available low-cost microcontrollers and microprocessors. The topological structure of the investigated asymmetrical multilevel converter is presented, and analytical relationships are established to define a general design principle of a uniform step configuration. Then, the proposed method's working principle and its generalized implementation scheme are presented and discussed. The effectiveness of the suggested PWM technique is finally demonstrated by simulation and experimental results for a single-phase, three-cell asymmetric converter operating under unequal DC-link voltages with 7, 11, 13, and 15 voltage levels.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.934
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
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.274
Teacher spread0.229 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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