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Record W4402774346 · doi:10.1109/access.2024.3462255

Electromagnetic Transient Modeling of Asynchronous Machine in Modelica, Accuracy, and Performance Assessment

2024· article· en· W4402774346 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldComputer Science
TopicModeling and Simulation Systems
Canadian institutionsPolytechnique MontréalHydro-Québec
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsModelicaTransient (computer programming)Computer scienceAsynchronous communicationTransient analysisComputational electromagneticsControl engineeringTransient responseProgramming languageElectromagnetic fieldElectrical engineeringEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Classical EMT-type simulators are mostly programmed in procedural languages, e.g. Fortran or C. In these languages, the focus is mainly on the solution methods. Modern languages, such as Modelica, are declarative and primarily focused on modeling and simulation. Modelica offers a much higher abstraction level, which makes the codes more concise and understandable. This paper contributes to the electromagnetic transient modeling and simulation of asynchronous machines in Modelica. In this paper, the modeling of a three-phase squirrel cage (single and double cage) and wound-rotor induction machine in three different reference frames is described and implemented. The accuracy and performance of Modelica models are compared and validated with the classical modeling approach used in the reference software EMTP. It is demonstrated that Modelica-based models with variable-step solvers offer fast and accurate results for time-domain simulations of motor sequential startup cases.

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 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 score0.502
Threshold uncertainty score0.424

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 communication0.0000.001
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 head0.033
GPT teacher head0.319
Teacher spread0.286 · 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