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Record W2000778068 · doi:10.1109/ccece.2012.6334817

Modified nearest three virtual space-vector modulation method for improved dc-capacitor voltage control in N-level diode clamped inverters

2012· article· en· W2000778068 on OpenAlexaff
B. Mohamadiniaye Roodsari, Arif Al‐Judi, E.P. Nowicki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInverterCapacitorDuty cycleControl theory (sociology)MATLABVoltageElectronic engineeringComputer scienceDigital signal processingModulation indexSpace vector modulationModulation (music)EngineeringElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

A major disadvantage of diode clamped inverters (DCI) in practical implementations is capacitor dc voltage fluctuation. A new and very fast space vector modulation scheme for controlling the capacitor dc voltage fluctuation is introduced in this paper. The proposed algorithm accelerates the computation of the nearest three vectors and calculation of corresponding duty cycles without an increase in computational requirements when extended to DCIs with more levels. This new algorithm is appropriate for real time applications with DSP controllers used in industry. Performance of the proposed method has been simulated in MATLAB Simulink for both 3-level and 4-level three-phase DCIs, with experimental verification presented using a TMS320F2812 DSP. Experimental implementations demonstrate that the proposed method has the ability to effectively control dc-capacitor voltage in an N-level DCI where N is the number of levels (including zero volts) in one half cycle of the inverter line-to-line output voltage.

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: Empirical · Consensus signal: none
Teacher disagreement score0.957
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 communication0.0000.001
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 head0.034
GPT teacher head0.249
Teacher spread0.215 · 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
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

Citations6
Published2012
Admission routes1
Has abstractyes

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