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Record W2012976144 · doi:10.1109/epec.2013.6802912

Distributed generation grid connection experiences minimizing high voltage equipments

2013· article· en· W2012976144 on OpenAlexaboutno aff
Aidan Foss, Kalle Leppik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsnot available
Fundersnot available
KeywordsRecloserTransformerGridEngineeringIslandingElectrical engineeringHigh voltageVoltageGroundGrid codeLow voltageReliability engineeringDistributed generationCircuit breakerAC powerRenewable energy

Abstract

fetched live from OpenAlex

Since 2007, farm-based biogas utilizing small synchronous generation has emerged in Ontario. Initial attempts at grid connection revealed several costly barriers. First, a requirement for proponents to pay for consequential upgrades to distribution infrastructure was mitigated through an amendment to the Ontario Distribution Code. Second, a requirement for transfer-trip to protect against feeder islanding was removed through the evolution of a low-cost alternative involving passive protections. Third, high-voltage equipments, contained in the standard grid connection designs, were considered disproportionately expensive. To minimize the use of such equipments: For connections to three-wire feeders, fast imbalance protection was employed to avoid using high-voltage potential transformers; For connections to feeders employing single-phase reclosers, the effective grounding requirement was relaxed, enabling high-voltage equipments associated with wye-delta transformation to be avoided; For connections to feeders with ganged three-phase reclosers, high-voltage equipments were avoided through utilizing a low-voltage wye-delta transformer for grounding.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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