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Record W4376467181 · doi:10.1109/tpwrd.2023.3275221

Optimized Reduced Jacobian Formulation for Simultaneous Solution of Control Systems in Electromagnetic Transient Simulations

2023· article· en· W4376467181 on OpenAlexaff
Boris Bruned, Jean Mahseredjian, S. Dennetière, Nicolas Bracikowski

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2023
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsJacobian matrix and determinantTransient (computer programming)Control theory (sociology)Electric power systemBlock diagramBlock (permutation group theory)HeuristicsModel order reductionMathematicsComputer sciencePower (physics)Mathematical optimizationAlgorithmEngineeringApplied mathematicsControl (management)PhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This article proposes a new optimized and reduced order Jacobian formulation for the simultaneous solution of control system block-diagrams with feedback loops in the simulation of electromagnetic transients. A reduced order Jacobian matrix formulation has been previously introduced for block-diagrams by solving a minimum Feedback Arc Set (FAS) problem. This article enhances the latter method by proposing a faster formulation and exploring efficient heuristics for the FAS problem. This optimized reduced Jacobian formulation is implemented in a generic tool for the simulation of electromagnetic transients and demonstrated for large-scale and practical power systems.

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 categoriesMeta-epidemiology (narrow)
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 score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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.

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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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