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Record W2963714928 · doi:10.1109/tcns.2019.2921347

The Effect of Transmission-Line Dynamics on Grid-Forming Dispatchable Virtual Oscillator Control

2019· article· en· W2963714928 on OpenAlexaff
Dominic Gros, Marcello Colombino, Jean-Sébastien Brouillon, Florian Dörfler

Bibliographic record

VenueIEEE Transactions on Control of Network Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsDispatchable generationControl theory (sociology)Electric power transmissionElectric power systemController (irrigation)Computer scienceTransmission linePower controlGridTransmission (telecommunications)Exponential stabilityPower (physics)MathematicsEngineeringControl (management)Electricity generationPhysicsTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, we analyze the effect of transmission line dynamics on grid-forming control for inverter-based ac power systems. In particular, we investigate a dispatchable virtual oscillator control (dVOC) strategy that was recently proposed in the literature. When the dynamics of the transmission lines are neglected, that is, if an algebraic model of the transmission network is used, dVOC ensures almost global asymptotic stability of a network of ac power inverters with respect to a prespecified solution of the ac power-flow equations. While this approximation is typically justified for conventional power systems, the electromagnetic transients of the transmission lines can compromise the stability of an inverter-based power system. In this paper, we establish explicit bounds on the controller set-points, branch powers, and control gains that guarantee almost global asymptotic stability of dVOC in combination with a dynamic model of the transmission network.

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)0.005
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.002
GPT teacher head0.173
Teacher spread0.171 · 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

Citations159
Published2019
Admission routes1
Has abstractyes

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