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Record W2125028769 · doi:10.1109/pes.2006.1708970

IED modelling for IEC61850 based substation automation system performance simulation

2006· article· en· W2125028769 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

Venue2006 IEEE Power Engineering Society General Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicPower Systems and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsAutomationNetwork topologyComputer scienceSoftware deploymentConstruct (python library)Network simulationEmbedded systemProcess automation systemSystems engineeringComputer networkEngineeringSoftware engineering

Abstract

fetched live from OpenAlex

This paper introduces the modeling of intelligent electronic device (IED) on OPNET modeler, a communication network modeling and simulation tool developed by OPNET TECHNOLOGIES. These configurable IED models allow to easily build substation automation system (SAS) network model with different topologies for all kinds of substations so that the dynamic performance issues could be studied during the planning stage and network performance problem could be caught ahead of the deployment stage. An example of using those models to construct SAS network on OPNET Modeller as well as the network performance simulation results is also included in this paper.

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 score0.493
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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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