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Record W2040839389 · doi:10.1109/ecce.2012.6342701

Introducing the Natural Switching Surface for reference frame systems: Three-phase boost PFCs

2012· article· en· W2040839389 on OpenAlexaff
Juan M. Galvez, Martin Ordonez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsControl theory (sociology)Total harmonic distortionComputer scienceTransient (computer programming)TrajectoryRectifier (neural networks)Distortion (music)Reference frameFrame (networking)Boundary (topology)Steady state (chemistry)HarmonicThree-phaseFrequency domainStationary Reference FrameHarmonic analysisControl (management)Electronic engineeringVoltageEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper presents the boundary control of three-phase boost rectifiers, bringing unprecedented dynamic response to reference frame based systems. A novel approach using the Natural Switching Surface (NSS) in the normalized αβ domain is introduced, providing superior dynamic performance and low input harmonic distortion. The derivation of the natural trajectories in the aforementioned domain is first performed to gain insight into the behavior of the system, yielding a generalized analysis that is valid for any possible combination of converter parameters. The target operating trajectory produced by the varying references is also described and characterized. Based on the previous analysis, the control laws that rule the behavior of the active rectifier are proposed, achieving constant-frequency operation as part of the control scheme. The resulting strategy provides excellent transient behavior, reverting to steady state in a few switching cycles when the PFC is subject to sudden load disturbances. Experimental and simulation results of a small scale 50W converter are presented to verify the enhanced properties of the NSS.

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 score0.897
Threshold uncertainty score0.566

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 head0.018
GPT teacher head0.264
Teacher spread0.247 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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