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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 OpenAlex
Mohamed Shawky El Moursi

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
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