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Record W1982947382 · doi:10.1109/pesmg.2013.6672734

A real-time dynamic simulation tool for transmission and distribution power systems

2013· article· en· W1982947382 on OpenAlexaff
Vahid Jalili-Marandi, Fábio J. Ayres, Esmaeil Ghahremani, Jean Bélanger, Vincent Lapointe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsSCADAComputer scienceReal-time simulationPower system simulator for engineeringTransmission (telecommunications)Electric power systemSoftwarePower (physics)Transmission systemReal-time Control SystemSimulationReal-time computingControl engineeringEmbedded systemEngineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

ePHASORsim tool offers real-time dynamic simulations for transmission and distribution power systems. Applications such as contingency studies, testing control devices, operator training, and SCADA system tests are examples for employing this tool. This paper describes the hardware and software architecture of the ePHASORsim and its development. The accuracy of the tool has been evaluated in comparison to other commercial, but non-real-time, simulation packages for both transmission and distribution systems. Its real-time performance has been tested with a time-step of 10ms on a real-time simulator for large-scale power systems in the order of 20000 buses, 5000 generators, and over 9000 control devices.

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 score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.003

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 head0.004
GPT teacher head0.211
Teacher spread0.207 · 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

Citations33
Published2013
Admission routes1
Has abstractyes

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