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Record W2135387253 · doi:10.1109/tpwrd.2004.835422

Probabilistic Models of Harmonic Currents Produced by Twelve-Pulse AC/DC Converters

2004· article· en· W2135387253 on OpenAlex
É. Ngandui, Pierre Sicard

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 Delivery · 2004
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsConvertersProbabilistic logicHarmonicHarmonic analysisAmplitudeVoltagePulse-width modulationPulse (music)Electronic engineeringControl theory (sociology)Power (physics)Monte Carlo methodElectrical engineeringPhysicsEngineeringComputer scienceMathematicsAcousticsStatisticsOptics

Abstract

fetched live from OpenAlex

The probabilistic approach is an efficient mean for harmonic prediction since it allows to take into account the randomly varying operating conditions of static converters. Although the six-pulse ac/dc converter has been the subject of probabilistic modeling, little consideration has been given to the 12-pulse ac/dc converter. In high-power applications, such as large rated dc power supplies and large rated dc and ac drives, 12-pulse ac/dc converters are usually preferred since they allow to reduce system disturbances due to harmonic currents. This paper presents an analysis of the random properties of harmonic currents produced by 12-pulse ac/dc converters that operate under random variations of their dc load and voltage unbalance. The probability density functions of the amplitudes and phase angles of the harmonic currents (of orders 12 k/spl plusmn/1 and 12 k-6/spl plusmn/1) are established and validated by results obtained from Monte Carlo simulations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.977

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 head0.012
GPT teacher head0.205
Teacher spread0.194 · 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