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Record W2285932123 · doi:10.1109/iciinfs.2015.7399020

Extracting data from the RSCAD electromagnetic simulation platform to perform eigenvalue analysis

2015· article· en· W2285932123 on OpenAlexafffund
B. W. H. A. Rupasinghe, U.D. Annakkage

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Manitoba
FundersMitacs
KeywordsSoftwareEigenvalues and eigenvectorsComputer scienceAlgorithmProgramming languagePhysics

Abstract

fetched live from OpenAlex

RSCAD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> is an Electromagnetic Transient (EMT) simulation software of the RTDS real-time digital simulator. Currently, the software is able to carry out detailed nonlinear time-domain simulations only. Small signal stability analysis (eigenvalue analysis) of a power system cannot be performed on the software itself. Exporting data of a simulation case from RSCAD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , in order to perform eigenvalue analysis on a separate commercially available software, is also not trivial. The demonstration of extracting data of an RSCAD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> simulation case, to perform eigenvalue analysis, is presented. The data is extracted from the files, generated by RSCAD, which the user has access to. Therefore the data extraction and eigenvalue analysis can be done by an external software module as presented. Incorporating the software module to RSCAD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> as an internal function is proposed. The software module can perform eigenvalue analysis if all the power system models in the case are generic models, as is the case with commercially available software like SSAT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> . Additionally, the proposed module can extract data of user-written linear controller models, and successfully incorporate them to the state space eigenvalue analysis. For full functionality of the module, all generic models should be linearized and included in the database. A common protocol is proposed for future expansion of the module. The extracted data is written into widely accepted file formats in the industry so that RSCAD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> data can be exported for interoperability between different software.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.302
Teacher spread0.198 · 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 teacher head, 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

Citations0
Published2015
Admission routes2
Has abstractyes

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