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Record W4293080407 · doi:10.1049/icp.2022.1117

Comparative study of Swing Equation-based and full emulation-based Virtual Synchronous Generators

2022· article· en· W4293080407 on OpenAlexaff
Changmin Jiang, Ajinkya Sinkar, A.M. Gole

Bibliographic record

VenueIET conference proceedings. · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEmulationSwingComputer scienceSynchronous motorTransient (computer programming)Voltage sourceElectrical impedanceVoltagePermanent magnet synchronous generatorControl theory (sociology)Representation (politics)Generator (circuit theory)Electric power systemPower (physics)Control engineeringElectronic engineeringEngineeringElectrical engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper discusses two different implementations of a Virtual Synchronous Generator (VSG) type voltage source converter (VSC). Both models use the swing equation dynamics but differ in the representation of the electrical part. The first, referred to as the Swing Equation (SE) based VSG uses a simplified representation of a voltage source behind an impedance, while the second uses a detailed transient model of a synchronous machine and is referred to as a Full Synchronous Machine (SM) Emulation VSG. The detailed model is validated by comparison with an actual synchronous machine. Using a simple test system, comparison of the mechanical dynamics is performed for step changes in power order and faults. It is shown that the two approaches have nearly equivalent performance if the damping is properly represented in the SE-based approach. Also, it is shown that current limit is more readily incorporable in the Full SM Emulation based approach than in the SE-based approach.

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)0.000
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: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.693

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 communication0.0000.000
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 head0.020
GPT teacher head0.222
Teacher spread0.201 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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