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Record W2014813100 · doi:10.1109/icems.2005.202780

Space Vector Modulation with Flexible Three-Segment Switching Sequence for Five-Level Inverters

2005· article· en· W2014813100 on OpenAlexaff
Zhongy, Bin Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSequence (biology)WaveformSpace vector modulationControl theory (sociology)AlgorithmSupport vector machineComputer scienceState (computer science)Modulation (music)State vectorVoltageTopology (electrical circuits)MathematicsArtificial intelligenceEngineeringPulse-width modulationTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper presents a space vector modulation (SVM) algorithm with flexible 3-segment switching sequence and its application to five-level inverters. In the flexible 3-segment SVM, the leading state of the switching sequence is selected according to minimum change of state value to ensure minimum switchings when the space vector hops between different triangles in the vector plane, and the switching sequence is generated in a flexible way according to the state value of the leading state, rather than using predefined patterns as most conventional SVM schemes do. The algorithm is simplified and the waveform symmetry is guaranteed by a simple `state rotation and inversion' to reproduce the switching sequence of Sector I for the other sectors. Simulation results show that the flexible 3-segment scheme can minimize or eliminate extra switchings. For a given switching frequency, the proposed 3-segment scheme yields lower WTHD than the conventional 7-segment scheme. Experimental spectrum of the output voltage verifies the simulation 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 categoriesnone
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 score0.916
Threshold uncertainty score0.718

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 head0.044
GPT teacher head0.241
Teacher spread0.197 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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