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Record W2153149041 · doi:10.1109/intlec.1997.645922

A new control strategy for DC voltage regulation and VAr generation using PWM three-phase rectifier

2002· article· en· W2153149041 on OpenAlexaff
N. Mendalek, Kamal Al‐Haddad, Ambrish Chandra, R. Parimelalagan, V. Rajagopalan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsControl theory (sociology)Feed forwardPulse-width modulationOperating pointPWM rectifierRectifier (neural networks)Computer scienceSmall-signal modelOpen-loop controllerVoltageEngineeringControl engineeringElectronic engineeringClosed loopControl (management)

Abstract

fetched live from OpenAlex

The basic PWM boost converter with six bidirectional current switches has been used as a reactive power generator or as a controlled rectifier and many control strategies have been developed for these two applications. A closed loop control of the dual-mode operation combining the VAr generation and the DC link voltage regulation, using a single converter is considered in this paper. The details of such a closed loop control based on the state space model, using synchronous d-q frame variables, are presented in order to achieve stable operation with improved dynamic response. During the design, the input reactor and the carrier frequency are at first selected based on open loop simulations. In order to compensate for the known nonlinearity in the state space model and facilitate the computation of the operating point variables and the feedforward terms; a gain scheduling technique is applied on one of the state variables. The feedback control is applied on the small signal model about the operating point, using pole-placement technique. Simulation results validating the closed loop operation of the complete system are also presented.

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 score0.974
Threshold uncertainty score0.577

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 head0.068
GPT teacher head0.258
Teacher spread0.190 · 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
Published2002
Admission routes1
Has abstractyes

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