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An Electrical Stall Control Algorithm for Small-Scale Wind Generation System using Aerodynamic Observer

2020· article· en· W3104634751 on OpenAlexaff
Guanhong Song, Bo Cao, Liuchen Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsControl theory (sociology)Stall (fluid mechanics)Wind speedWind powerAerodynamicsTurbineRotational speedTip-speed ratioComputer scienceControl systemEngineeringAutomotive engineeringAerospace engineeringMeteorologyControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

In a small-scale fixed-pitch variable-speed wind generation system, the power production has to adapt to various wind conditions by varying its rotation speed to achieve the maximum power extraction. However, this maximum power extraction behavior may result to over-rated operation of the system in high wind speed regions, where wind speed to the system is higher than its rated wind speed. Therefore, the power production of the system has to be regulated by certain stall control algorithm within system constraints in order to achieve long term operation in high wind speed regions. In this paper, a novel electrical stall control algorithm using an aerodynamic observer is proposed for small-scale wind generation systems to perform power curtailment and stall regulation of the wind turbine when operating in high wind speed conditions. By integrating of the aerodynamic torque observer and the additional stall control loop into the system, the operation of the wind turbine can be estimated and controlled to achieve desired stall regulation. Detailed control system analysis and simulation results have been presented in this paper to demonstrate the effectiveness of the proposed algorithm.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.892

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 head0.025
GPT teacher head0.210
Teacher spread0.185 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2020
Admission routes1
Has abstractyes

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