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Nonlinear Integral Backstepping Control of Machine Side Converter PMSG Wind Turbine Conversion System During Grid Faults

2024· article· en· W4408793081 on OpenAlexaff
Youcef Saidi, Brahim Brahmi, Abdelkader Mezouar, Meddah Atallah, Mohammed Amine Benmahdjoub

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsCollege Ahuntsic
Fundersnot available
KeywordsBacksteppingControl theory (sociology)Nonlinear systemTurbineGridWind powerComputer scienceControl (management)Control engineeringEngineeringGeologyElectrical engineeringPhysicsAdaptive controlMechanical engineering

Abstract

fetched live from OpenAlex

To solve the problem of balanced power grid voltage faults in Wind Turbine Conversion Systems (WTCS) based on Permanent Magnetic Synchronous Generator (PMSG), an advanced method solution based on Machine Side Converter Control (MSC) using the Integrated Back-Stepping Control (IBSC) is proposed in this paper. This nonlinear IBSC technique effectively addresses the harmonics of the stator current in the classical Proportional Integral control (PI) and enhances the dynamic performance of the MSC such as the DC link voltage controls during grid faults. The suggested IBSC method and its efficacy in comparison to the traditional PI method are demonstrated by time-domain simulation tests conducted on a WTCS-driven PMSG utilizing MATLAB/Simulink.

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: Empirical
Teacher disagreement score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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