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Record W2039760745 · doi:10.1109/tpwrd.2006.886788

Modelling and Simulation for Performance Evaluation of IEC61850-Based Substation Communication Systems

2007· article· en· W2039760745 on OpenAlexaff
T.S. Sidhu, Yujie Yin

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2007
Typearticle
Languageen
FieldEngineering
TopicPower Systems and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsIEC 61850InteroperabilityNetwork topologyAutomationSystems engineeringConstruct (python library)ExtensibilityComputer scienceTelecommunications networkNetwork simulationEngineeringEmbedded systemSoftware engineeringDistributed computingComputer networkOperating system

Abstract

fetched live from OpenAlex

With the publishing of IEC 61850, the global communication standard for substation automation system (SAS), interoperability issue between intelligent electronic devices (IEDs) from different venders has been resolved. However, the overall performance and extensibility of this communication network are still left unanswered. This paper introduces the modeling technique of IED generic models and the setup of a research platform for resolving those issues using the OPNET Modeler. These configurable IED models allow the engineers to easily build SAS network model with different topologies for all kinds of substations so that the dynamic performance issues could be studied and rules could be developed to guide the SAS network planning and design. Some examples of using those models to construct SAS network as well as the network performance simulation results are 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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.260
Teacher spread0.219 · 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 designSimulation or modeling
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".

Quick stats

Citations225
Published2007
Admission routes1
Has abstractyes

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