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Record W2467907599 · doi:10.1109/tpel.2016.2585576

Current-Fed Multilevel Converters: An Overview of Circuit Topologies, Modulation Techniques, and Applications

2016· article· en· W2467907599 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2016
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
FundersComisión Nacional de Investigación Científica y TecnológicaChina Scholarship CouncilEnergy Market Authority of Singapore
KeywordsConvertersNetwork topologyModulation (music)WaveformComputer scienceElectronic engineeringVoltageLow voltageElectrical engineeringTopology (electrical circuits)EngineeringPhysics

Abstract

fetched live from OpenAlex

Multilevel converters (MLCs) have emerged as standard power electronic converters in high power as well as quality demanding applications. They are classified into current-fed MLCs and voltage-fed MLCs. Voltage-fed MLCs have widely researched whereas the current-fed MLCs are the recent topic of research. Based on the principle of duality between voltage and current sources, several current-fed MLCs analogous to voltage-fed MLCs have been identified. Current-fed MLCs offer several advantages in terms of high power capability, transformerless operation, short-circuit protection, and excellent quality of output current waveform. The goal of this paper is: 1) to present review of circuit topologies, modulation schemes, and applications of current-fed MLCs; and 2) to review an emerging low-device switching frequency modulation technique known as synchronous optimal pulsewidth modulation for current-fed MLCs. The circuit configuration and advantages of each topology along with various modulation techniques are discussed in detail. Compared to voltage-fed MLCs, the operation of current-fed MLCs need to satisfy additional switching constraints. A survey of classical methods for realization of these operational constraints has been done and a new generalized method has been proposed. Finally, future scope of research has been presented to encourage further development of topologies and modulation techniques for current-fed MLCs.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.705

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 head0.038
GPT teacher head0.277
Teacher spread0.239 · 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