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Record W2170988611 · doi:10.1109/pes.2008.4596673

Adaptive protection and control strategy for interfacing wind-power electricity generators to distribution grids

2008· article· en· W2170988611 on OpenAlexaff
Alexander Hamlyn, Helen Cheung, Lin Wang, Cungang Yang, Richard Cheung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInterfacingWind powerInduction generatorElectricity generationComputer scienceDistributed generationMicrogridFault (geology)Control engineeringSCADAEngineeringRenewable energyElectrical engineeringPower (physics)Computer hardware

Abstract

fetched live from OpenAlex

Recently electricity generation from wind power has been increasingly popular worldwide. This paper proposes an adaptive protection and control strategy for interfacing the wind-powered distributed generators into the utility power distribution grids. The requirements for the interfacing are defined according to the IEEE-1547 standards. This paper presents the adaptive interfacing controls and protections for three common types of wind-powered generators: doubly-fed induction generators, permanent-magnet synchronous generators, and squirrel-cage induction generators. The design for the adaptive protection and control presented in this paper uses state-of-the-art digital signal processing technology and modern computer networking technology. This paper presents a new two-layer computer network architecture for real-time monitoring the operations as well as the impacts of wind-powered electricity generations in the utility distribution systems. This architecture is fault tolerant and is designed for monitoring power distribution systems with multiple (over hundred) feeder nodes.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.208
Teacher spread0.190 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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