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Design and implementation of a cross-platform sensor network for smart grid transmission line monitoring

2011· article· en· W2026528127 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBlackoutComputer scienceSmart gridRelayGridElectric power transmissionTransmission (telecommunications)Distributed computingNetwork topologyUnified Modeling LanguageFault (geology)Power (physics)Computer networkElectric power systemTelecommunicationsElectrical engineeringEngineeringOperating system

Abstract

fetched live from OpenAlex

Slow information feedback and hesitation to relay the information to other power stations was a major contributor to North America's northeast blackout of 2003. Had fault information been properly passed on, the blackout area would have been much smaller. From experience, it can be seen that having the knowledge of knowing exactly when and what is happening on the power distribution grid is extremely valuable. This paper proposes and develops a practical, self configuring, sampling and forwarding scheme for smart grid transmission line monitoring and presents a hierarchical communication topology. The developed prototype operates successfully and its functionality is documented in detail. In addition, descriptive Unified Modelling Language (UML) diagrams are presented to allow for understanding and implementation of the concepts behind our proposed design.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.931
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.067
GPT teacher head0.312
Teacher spread0.245 · 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

Quick stats

Citations11
Published2011
Admission routes1
Has abstractyes

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