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Record W2155400858 · doi:10.1260/0309-524x.34.2.219

Generalized Gain Scheduling for Deloaded Wind Turbine Operation

2010· article· en· W2155400858 on OpenAlex

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

VenueWind Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsPolytechnique MontréalOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsTurbineGain schedulingControl theory (sociology)Voltage droopWind powerLinearizationScheduling (production processes)EngineeringComputer scienceControl engineeringControl systemNonlinear systemElectrical engineeringAerospace engineeringVoltageControl (management)

Abstract

fetched live from OpenAlex

The ability to produce less power than what is available from a wind source, a condition known as deloaded operation, is needed for a wind turbine to reproduce synchronous machine behavior in terms of inertial response and frequency droop regulation. Deloaded operation requires the ability to regulate both power production and rotor speed under any wind speed conditions. In this paper, a novel controller for deloaded wind turbine operation is presented. This controller is made possible by a Cp table inversion procedure allowing generalized gain scheduling for linearization of the pitch response. After introducing the wind turbine models, a review of classical turbine control principles and the proposed deloaded wind turbine control architecture is presented. A discussion of wind turbine non linearity and linearization principles follows. Simulation results are shown for stability, immunity to icing and performance. The advantages of generalized gain scheduling over classical gain scheduling are demonstrated by simulation results.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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