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Record W2029666689 · doi:10.1109/tpwrs.2013.2284670

An Autoground System for Anti-Islanding Protection of Distributed Generation

2013· article· en· W2029666689 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2013
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsHydro-Québec
FundersHydro-QuébecAalborg Universitet
KeywordsIslandingRecloserOvercurrentDistributed generationDistributed power generationContext (archaeology)PhasorController (irrigation)EngineeringReliability engineeringScalabilityElectric power systemComputer scienceControl engineeringElectrical engineeringCircuit breakerPower (physics)VoltageRenewable energy

Abstract

fetched live from OpenAlex

Due to the variety of distribution generation (DG) sizes and technologies connecting to distribution networks, and the concerns associated with out-of phase reclosing, anti-islanding continues to be an issue where no clear solution exists. This paper presents an autoground approach that was proposed in the context of an IEEE working group on best practices for DG protection. A prototype system was constructed using standard distribution apparatus and a recloser controller, and it was tested on the utility's distribution test line. Results show that the anti-islanding detection time is approximately a cycle longer than the delay associated with application of the autoground. Once the autoground was applied, the DG was disconnected within 1 cycle on overcurrent protection. The solution is inherently scalable, applicable to all DG types, is configurable to various reclosing practices and does not require additional equipment or settings changes at the producer's site.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.884
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.218
Teacher spread0.201 · 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