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Record W2090644752 · doi:10.2202/1553-779x.1558

Automatic Gain Controller for Variable Speed Wind Turbines

2008· article· en· W2090644752 on OpenAlexaff
Mohamed Shawky El Moursi

Bibliographic record

VenueInternational Journal of Emerging Electric Power Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Transient (computer programming)Wind powerAC powerElectric power systemTransient voltage suppressorEngineeringTransient responseVoltageComputer scienceControl engineeringPower (physics)Control (management)Electrical engineering

Abstract

fetched live from OpenAlex

This paper presents a novel controller for DFIG based wind parks, designed to achieve more efficient voltage regulation, reactive power compensation and to enhance the transient stability margin of the interconnected power system. The supervisory-secondary voltage control is used to generate the local voltage reference, providing an improved overall voltage profile, while combining an automatic gain controller (AGC) to improve the transient response of the primary control loop. The controller is implemented and tested with a power system comprising of a lumped, fundamental frequency model of a DFIG based wind park, and hydro and diesel generators connected to the electric grid. The performance of the controller was investigated for both steady-state improvements as well as under extreme contingencies to demonstrate its benefits.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2008
Admission routes1
Has abstractyes

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