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Record W2146973638 · doi:10.1109/tpwrs.2007.901308

Re-examination of Synchronous Machine Modeling Techniques for Electromagnetic Transient Simulations

2007· article· en· W2146973638 on OpenAlexaff
Liwei Wang, Juri Jatskevich, H.W. Dommel

Bibliographic record

VenueIEEE Transactions on Power Systems · 2007
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReactanceEmtpTransient (computer programming)Electric power systemDiscretizationComputer scienceControl theory (sociology)Synchronous motorTime domainVoltageControl engineeringPower (physics)Electronic engineeringEngineeringElectrical engineeringMathematicsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper re-examines the three synchronous machine modeling techniques used for electromagnetic transient simulations, namely, the qd model, phase-domain model, and voltage-behind-reactance model. Contrary to the claims made in several recent publications, these models are all equivalent in the continuous-time domain, as their corresponding differential equations can be algebraically derived from each other. Computer studies of a single-machine infinite-bus system demonstrate that all of these models can be used for unsymmetrical operation of power systems. The conversion of machine parameters is also discussed and is shown to have some impact on simulation accuracy, which is acceptable for most cases. When the models are discretized and interfaced with an EMTP-type network solution, the voltage-behind-reactance model is shown to be the most accurate due to its advanced structure.

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)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.234
Teacher spread0.225 · 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

Citations84
Published2007
Admission routes1
Has abstractyes

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