MétaCan
Menu
Back to cohort
Record W2107338936 · doi:10.1109/tpwrd.2005.858801

Small-Signal Dynamic Model and Analysis of a Fixed-Speed Wind Farm—A Frequency Response Approach

2006· article· en· W2107338936 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

VenueIEEE Transactions on Power Delivery · 2006
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWind powerWind speedInduction generatorElectric power systemControl theory (sociology)SIGNAL (programming language)EngineeringTime domainPower system simulationFrequency responseGridSensitivity (control systems)Frequency domainSystem dynamicsElectrical networkPower (physics)Electronic engineeringComputer scienceElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper introduces a small-signal dynamic model of a fixed-frequency (induction machine based) wind farm connected to an electrical power system. The model represents the system small-signal dynamics in the frequency range of a fraction of Hz to 50/60 Hz. The model includes the dynamics of the wind energy capturing mechanism, the rotating shaft system, the generator electrical system of each wind energy unit, the wind farm collector system, and the utility grid. Based on the proposed model, two performance indices are introduced to i) investigate sensitivity of the system modes, e.g., torsional modes, to the system parameters, and ii) evaluate the system capability to reject electrical and mechanical disturbances, e.g., a wind gust. The application of the model to the analysis of a system with two wind units is presented and the study results are validated based on comparison with the time-domain simulation results obtained in the PSCAD/EMTDC environment.

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 score0.423
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
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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