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Record W2598370789 · doi:10.1049/iet-gtd.2016.1616

Development of a hybrid simulator by interfacing dynamic phasors with electromagnetic transient simulation

2017· article· en· W2598370789 on OpenAlexafffund
K. Mudunkotuwa, Shaahin Filizadeh, U.D. Annakkage

Bibliographic record

VenueIET Generation Transmission & Distribution · 2017
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of ManitobaResearch Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterfacingPhasorInterface (matter)Transient (computer programming)Computer scienceFlexibility (engineering)WaveformSimulationDynamic simulationModular designCo-simulationElectronic engineeringEngineeringElectric power systemComputer hardwarePower (physics)

Abstract

fetched live from OpenAlex

This study presents a method to interface an electromagnetic transient (EMT) simulator with a dynamic phasor (DP) simulation program. The hybrid DP–EMT simulator offers flexibility in deciding the harmonic contents to be preserved in the DP domain; additionally, it offers significant reduction in computing time of large networks compared with EMT simulation. The proposed interfacing method is based on a generic transmission line, which uses specialised numerical techniques to transfer discretised waveform samples between the two simulators. These numerical methods produce the required mapping between EMT simulation samples and their counterpart DPs. This study describes the mathematical foundations of the DP–EMT interface and demonstrates them using an illustrative example. Several large networks are then studied to assess the interface's accuracy and performance in reducing computing time.

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)0.000
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations33
Published2017
Admission routes2
Has abstractyes

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