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Record W2623817398 · doi:10.1109/jestpe.2017.2712620

Subfundamental Cycle Switching Frequency Variation Based on Output Current Ripple Analysis of a Three-Level Inverter

2017· article· en· W2623817398 on OpenAlexafffund
Subhadeep Bhattacharya, Sourabh Kumar Sharma, Diego Mascarella, G. Joós

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsCanadian Pacific Railway (Canada)McGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRippleInverterModulation indexControl theory (sociology)Pulse-width modulationCurrent (fluid)Modulation (music)Switching frequencyFrequency modulationComputer scienceVoltageEngineeringElectrical engineeringPhysicsBandwidth (computing)TelecommunicationsAcousticsControl (management)

Abstract

fetched live from OpenAlex

The output current ripple of an inverter shows different variations for sub-fundamental operating cycle and over few fundamental cycles. This paper investigates subfundamental cycle peak-peak current ripple variation of a three-level three-phase inverter over the entire operating modulation index range for the three-level continuous and discontinuous pulsewidth modulation strategies. Furthermore, based on the analytical current ripple variation, a strategy is presented to change the switching frequency within a fundamental cycle while maintaining the peak-peak current ripple similar to the conventional fixed switching frequency strategies. The strategy has been implemented and experimentally validated on a permanent-magnet synchronous motor drive. The results demonstrate that the proposed strategy reduces inverter switching instances and associated losses around 10-30%.

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: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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

Citations23
Published2017
Admission routes2
Has abstractyes

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