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Record W2130314611 · doi:10.1109/pesc.1994.349739

Generalized switching strategies for current source inverters/converters

2002· article· en· W2130314611 on OpenAlexaff
H.A. Kojori, Hamid Reza Karshenas, S.B. Dewan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPulse-width modulationConvertersWaveformHarmonicsHarmonicModulation (music)Power (physics)VoltageControl theory (sociology)Current (fluid)InverterCurrent sourceHarmonic analysisComputer scienceTopology (electrical circuits)Electronic engineeringPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper presents generalized techniques for realizing PWM patterns which provide selective harmonic elimination and current magnitude modulation (SHEM) for current source inverters/power converters (CSI/C). Recently, PWM-SHEM patterns for CSI/C have been presented which utilize a combination of chops and short circuit pulses to selectively eliminate lower order harmonics besides achieving current magnitude modulation with minimum switching frequency. In the present paper, these PWM-SHEM patterns are reviewed and development of any arbitrary PWM-SHEM is discussed in detail. In particular, the requirements of maximum inverter/power converter utilization are examined and different modes of operation (i.e., rectifying and inverting) are explained. Furthermore, the generalized equations for the input/output AC currents for the CSI/C are utilized to derive a generalized equation for the DC input/output voltage of the CSI/C. Generalized waveforms and tables which show the relationship of various PWM-SHEM parameters to the position of short circuit pulses and the number of chops per 30/spl deg/, are provided and discussed in detail.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 score0.967
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.231
Teacher spread0.195 · 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
Published2002
Admission routes1
Has abstractyes

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