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Record W2560236144 · doi:10.1109/epec.2016.7771791

Anti-islanding protection relay for medium voltage feeder with distributed generator

2016· article· en· W2560236144 on OpenAlexaff
Huafeng Xiao, Zhijian Fang, Chushan Li, Dewei Xu, Bala Venkatesh, Birendra N. Singh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIslandingRelayTransformerBackupVoltageElectrical engineeringProtective relayDistributed generationEngineeringThyristorComputer sciencePhysicsPower (physics)Renewable energy

Abstract

fetched live from OpenAlex

Large distributed generators such as solar farms and wind farms are usually connected to medium voltage feeders. The anti-islanding protection for medium-voltage (typically up to 50 kV) DG relies on the transfer trip from transformer station. This paper presents a local anti-islanding protection relay as a backup for transfer trip in case of failures. The anti-islanding detection scheme is to short the phase or line voltage at the point of common coupling when voltage crossing zero, and then detects the current in thyristor to determine the operating mode of the DG units. This proposed scheme has been evaluated using analytical, simulation and experimental test. The results show that the proposed scheme features high performance, low cost and high neutrality. It has the ability to protect against islands operation of DG units.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.194
Teacher spread0.183 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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