MétaCan
Menu
Back to cohort

Control Algorithm for Energy Storage Integration with an Open-Winding Wind Turbine Generator

2021· article· en· W3212630043 on OpenAlexaff
Mehanathan Pathmanathan, Peter W. Lehn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPermanent magnet synchronous generatorWind powerTurbinePower optimizerComputer scienceAC powerControl theory (sociology)EngineeringTopology (electrical circuits)Electrical engineeringVoltageMaximum power point trackingControl (management)Mechanical engineering

Abstract

fetched live from OpenAlex

A power electronic converter topology and control strategy is presented which allows battery energy storage to be integrated into a wind turbine. The topology leverages an open-end-winding permanent magnet synchronous generator controlled by a dual two-level voltage source converter. The additional degrees of freedom inherent to the proposed system allow for simultaneous control of wind turbine speed and the power flow to the grid. These control objectives are achieved by a power sharing algorithm which distributes the voltage vectors of the two converters in order to achieve desired power flow. An active front end power converter with LCL filter is used to connect the wind turbine and battery to a three-phase electrical grid. Simulation results show the efficacy of the proposed system in regulating the power flow to the grid while simultaneously ensuring wind turbine speed control is achieved.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207