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Record W4220978208 · doi:10.18280/ejee.240102

A New Robust RST Controller Based on PSO Optimization for DFIG Wind Turbine

2022· article· en· W4220978208 on OpenAlex
Abdelkarim Chemidi, Mohamed Horch, Mohammed El Amin Bourouis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)Particle swarm optimizationTurbineDoubly fed electric machineAC powerController (irrigation)Wind powerComputer sciencePolynomialControl engineeringEngineeringControl (management)MathematicsVoltageAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a new approach to control active and reactive powers for DFIG wind power system is proposed. Where parameters of the RST controller are tuned by the Particle Swarm Optimization (PSO) algorithm. First the calculation of conventional RST polynomial is presented. The main goal of this study is to apply and compare the performances of two kinds of regulators (conventional RST and PSO-RST) for DFIG wind turbine system. A vector control of the DFIG is also presented in order to accomplish the control of active and reactive powers. The obtained results show the effectiveness and good performances of PSO-RST controller compared to the conventional RST in terms of reference tracking and disturbances rejection.

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.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.167
Teacher spread0.160 · 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