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Record W2141362426

On the compatibility of fault location approaches and distributed generation

2009· article· en· W2141362426 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.

Bibliographic record

Venue2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources Into the Power Delivery System · 2009
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsControl reconfigurationDistributed generationDistributed computingAutomationComputer scienceRenewable energyCompatibility (geochemistry)Electricity generationFault toleranceDistributed data storeEngineeringEmbedded systemPower (physics)Electrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Advanced distribution automation and the integration of renewable energy are two important initiatives in the push to revolutionize the power system. Active distribution networks—intelligent distribution networks that incorporate one or more of distributed generation, demand response, and energy storage into the operation of the distribution network—is a new concept that depends largely on the compatibility of these two initiatives. This paper considers the emergence of innovative protection practices, the motivation for their implementation, and analyzes whether distributed generation can be seamlessly integrated into these new constructs. Two specific applications are considered: automatic reconfiguration and automatic fault location. The basic theory behind each technology and a specific algorithm for automatic fault location is implemented. The system is simulated with and without distributed generation. Results indicate that a greater degree of coordination may be required.

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)0.001
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: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.198
Teacher spread0.180 · 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