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Record W2947309294 · doi:10.1109/tpwrd.2019.2919252

Stability of Algorithms for Electro-Magnetic-Transient Simulation of Networks With Switches and Non-Linear Inductors

2019· article· en· W2947309294 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2019
Typearticle
Languageen
FieldEngineering
TopicControl and Stability of Dynamical Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInductorPassivityPiecewise linear functionTrapezoidal ruleControl theory (sociology)Transient (computer programming)Lyapunov functionQuadratic equationStability (learning theory)Linear systemMathematicsAlgorithmComputer scienceNonlinear systemVoltageNumerical integrationMathematical analysisEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper extends the analysis of the stability of electromagnetic transient simulation algorithms to non-linear systems with switching elements and non-linear inductor branches. A theoretical analysis based on common quadratic Lyapunov function theory is used to investigate the stability of numerical algorithms for the simulation of lumped strictly passive switched circuits (LSPSC). It is proved that only when certain fundamental physical properties, i.e., passivity and invariance of Lyapunov energy function, are satisfied, the widely used trapezoidal method results in stable simulations of such networks for any time-step size. This is different from the simulation of linear time invariant systems where any real-world stable system has a stable simulation if an A-stable integration method (e.g., trapezoidal rule) is used. Subsequently, it is shown that the problem of simulating a piecewise linear inductor can be equivalent to simulating a LSPSC; and ergo its simulation with the trapezoidal rule is also stable.

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.

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 score0.463
Threshold uncertainty score0.491

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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