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Record W2147889003 · doi:10.1109/jpets.2015.2427370

Real-Time Simulation Technologies for Power Systems Design, Testing, and Analysis

2015· article· en· W2147889003 on OpenAlexfundno aff
M. D. Omar Faruque, Thomas Strasser, Georg Lauss, Vahid Jalili-Marandi, Paul Forsyth, Christian Dufour, Venkata Dinavahi, Antonello Monti, Panos Kotsampopoulos, J.A. Martínez, Kai Strunz, Maryam Saeedifard, Xiaoyu Wang, David R. Shearer, Mario Paolone

Bibliographic record

VenueIEEE Power and Energy Technology Systems Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsnot available
FundersCollege of ComputingNational Technical University of AthensTechnische Universität BerlinCarleton UniversityAustrian Institute of TechnologyUniversity of TorontoUniversitat Politècnica de CatalunyaUniversity of AlbertaNational and Kapodistrian University of AthensAin Shams UniversityRWTH Aachen UniversityUniversity of Texas at ArlingtonU.S. Department of EnergyGeorgia Institute of TechnologyFlorida State University
KeywordsInterfacingComputer scienceSoftwareTask (project management)Real-time simulationSalientState (computer science)Systems engineeringEmbedded systemComputer hardwareEngineeringOperating system

Abstract

fetched live from OpenAlex

This task force paper summarizes the state-of-the-art real-time digital simulation concepts and technologies that are used for the analysis, design, and testing of the electric power system and its apparatus. This paper highlights the main building blocks of the real-time simulator, i.e., hardware, software, input-output systems, modeling, and solution techniques, interfacing capabilities to external hardware and various applications. It covers the most commonly used real-time digital simulators in both industry and academia. A comprehensive list of the real-time simulators is provided in a tabular review. The objective of this paper is to summarize salient features of various real-time simulators, so that the reader can benefit from understanding the relevant technologies and their applications, which will be presented in a separate 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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.005

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 head0.020
GPT teacher head0.239
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
GenreMethods

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

Citations477
Published2015
Admission routes1
Has abstractyes

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