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Record W2147377358 · doi:10.1109/ccece.2005.1557259

Modeling of an islanding protection method for a hybrid renewable distributed generator

2006· article· en· W2147377358 on OpenAlexaff
Mylà ̈ne Robitaille, Kodjo Agbossou, Mamadou Lamine Doumbi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsIslandingRenewable energyPhotovoltaic systemGridDistributed generationMATLABElectrical engineeringComputer scienceVoltageWind powerAC powerGenerator (circuit theory)Automotive engineeringEngineeringPower (physics)Electronic engineering

Abstract

fetched live from OpenAlex

The Hydrogen Research Institute developed a hybrid renewable energy power system that uses a wind turbine, a photovoltaic array and a fuel cell. In order to connect this system to the grid, an islanding protection device is being developed. This paper presents the passive (U/O Voltage, U/O Frequency) and active (Sandia frequency shift and Sandia voltage shift) protection methods that were chosen to be added to the system. It also presents a Matlab/Simulink model of the protection device and the simulation results that were obtained using different critical operating conditions for which the clearing times can surpass those defined by the IEEE 1547 standard. Passive and active detection methods are combined in order to improve the efficiency of the islanding protection.

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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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

Citations31
Published2006
Admission routes1
Has abstractyes

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