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Record W2534368067 · doi:10.1109/epc.2007.4520347

Input-State Feedback Linearization Control of Two-Stage Matrix Converters Interfaced With High-Speed Microturbine Generators

2007· article· en· W2534368067 on OpenAlexaff
Mahmoud Hamouda, Kamal Al‐Haddad, Farhat Fnaiech, Handy Fortin Blanchette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPower factorControl theory (sociology)LinearizationConvertersGenerator (circuit theory)MATLABAC powerComputer scienceVoltagePower (physics)EngineeringElectrical engineeringControl (management)PhysicsNonlinear system

Abstract

fetched live from OpenAlex

In this paper the indirect two-stage matrix converter is used with the aim to convert a high frequency output voltage of a microturbine generator to a low-frequency voltage for utility use. Moreover, in order to allow a unity input power factor operation of the electrical generator, an input-state feedback linearization control-law is implemented to control the current flowing between the generator and the matrix converter's input side. Numerical simulations are carried out using matlab and similink software emphasize the two-stage matrix converter's capability in providing sinusoidal output voltage with low distortion. Moreover, the unity power factor operation of the generator is also verified over a wide variation of the active power.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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.

Study designBench or experimental
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

Citations12
Published2007
Admission routes1
Has abstractyes

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