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Modulated Predictive Current Control of PMSG-Based Wind Energy Systems

2020· article· en· W3021859351 on OpenAlexaff
Kristiyan Milev, Venkata Yaramasu, Apparao Dekka, Samir Kouro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsLakehead University
Fundersnot available
KeywordsControl theory (sociology)Permanent magnet synchronous generatorWind powerController (irrigation)AC powerModel predictive controlComputer scienceSpace vector modulationGridGenerator (circuit theory)VoltagePower (physics)EngineeringPulse-width modulationElectrical engineeringControl (management)MathematicsPhysics

Abstract

fetched live from OpenAlex

This paper presents an efficient modulated model predictive current control for direct driven wind energy system composed of surface-mounted permanent magnet synchronous generator and back-to-back connected voltage source converter. The proposed control method fulfils the generator-side control requirements such as maximum power point tracking, and grid-side control objectives such as DC-link voltage control and power factor correction. These objectives are achieved through the regulation of generator and grid currents with fast transient response, smooth steady-state and fixed switching frequency operation simultaneously. The proposed controller predicts the future behavior of generator and grid currents using quasi-exact discrete-time models and eight voltage vectors, and then evaluates them by two independent cost functions. Finally, the switching sequence is designed by the space vector modulation using three stationary voltage vectors corresponding to the optimal cost function. The performance of the proposed method is validated through the MATLAB simulations using a 750-kW wind energy system at different wind speeds and grid reactive powers.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.441

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 head0.012
GPT teacher head0.181
Teacher spread0.168 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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