MétaCan
Menu
Back to cohort
Record W2537266990 · doi:10.1109/iecon.2002.1185483

A new multiple-loops control scheme for a three-phase/switch/level PWM rectifier based on the input/output feedback linearization technique

2003· article· en· W2537266990 on OpenAlexfundno aff
Hadi Y. Kanaan, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)PWM rectifierPulse-width modulationFeedback linearizationScheme (mathematics)Rectifier (neural networks)LinearizationComputer scienceThree-phaseControl (management)VoltageEngineeringMathematicsNonlinear systemPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, a new nonlinear control system is developed and applied to a three-phase, three-switch, three-level AC-to-DC converter. The derivation of the proposed control law is based on a fourth-order low-frequency time-invariant nonlinear state model of the converter. This mathematical model, expressed in a rotating frame, is obtained using averaging techniques over a double time base. The proposed control law is designed on the basis of a multiple-loops control strategy, where an exact input-output feedback linearization is applied to the inner and outer loops. The regulators are designed in order to ensure the shaping of the line-currents, the regulation of the DC output voltage and the compensation of the DC load unbalance. The performances of the proposed control law in steady state and transient regimes are then analyzed by numerical simulations. In this purpose, a digital version of the converter is implemented in the Matlab/Simulink framework, using the switching function approach. The obtained simulation results show a nearly zero current harmonic distortion, a unity displacement factor, a low output voltage ripple, and a high robustness to DC load unbalance.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.028
GPT teacher head0.243
Teacher spread0.215 · 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
GenreMethods

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

Citations17
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced DC-DC ConvertersFrench-language works237,207