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Record W1831150869 · doi:10.1109/pesc.1994.349678

A neural network controlled unity power factor three phase PWM rectifier

2002· article· en· W1831150869 on OpenAlex
A. Insleay, Navid R. Zargari, G. Joós

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)Rectifier (neural networks)Power factorPWM rectifierController (irrigation)Pulse-width modulationTransient (computer programming)Computer scienceFilter (signal processing)Power (physics)Artificial neural networkPhysicsArtificial intelligenceRecurrent neural networkControl (management)

Abstract

fetched live from OpenAlex

The simplest method of operating three-phase PWM rectifiers is based on the use of offline PWM patterns. However, in this scheme the input power factor is less than unity and varies with the rectifier operating point due to the presence of the input LC filter. Furthermore, the response to transient conditions is slow and large current oscillations may occur due to the resonance of the filter. In this paper an online neural network (NN) controller, proposed to waveshape the input line currents, forces unity power factor operation and damps the low frequency resonance of the input filter. The proposed controller is insensitive to load/parameter variations thus resulting in a robust system. The performance of the proposed NN controller is verified by simulation.< <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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.994

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)0.0070.000

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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

Quick stats

Citations9
Published2002
Admission routes1
Has abstractyes

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