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Record W4243837668 · doi:10.1109/pica.1991.160621

Analysis of the load flow behaviour near a Jacobian singularity

2002· article· en· W4243837668 on OpenAlexafffund
F.D. Galiana, Zhihui Zeng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicAdvanced Differential Equations and Dynamical Systems
Canadian institutionsMcGill University
FundersMcGill University
KeywordsSingularityJacobian matrix and determinantFlow (mathematics)MathematicsBifurcationRelation (database)Control theory (sociology)Mathematical analysisApplied mathematicsTopology (electrical circuits)Computer sciencePhysicsGeometryArtificial intelligenceCombinatoricsNonlinear systemQuantum mechanics

Abstract

fetched live from OpenAlex

New theoretical results above the behavior of the load flow solution near a Jacobian singularity are presented. The principal result is the derivation of an analytic closed-form relation between the specified injections and the resulting voltages in the neighborhood of a singularity. This result is a companion to the conventional load flow sensitivity analysis which is valid only if the operating point is not at a Jacobian singularity. The new closed-form relation derived is theoretically important since it can predict and explain the main load flow phenomena observed through simulation analysis near a singularity. These are: the nonexistence of solutions for certain injection changes, the bifurcation of the voltages into two nearby solutions, the sudden collapse of voltages for small injection changes, and the nature of the collapse, that is, which buses are more susceptible to the collapse. Numerical simulations support the validity of the theoretical result by comparing the closed-form analytic relation near a singularity with exact load flow simulations.>

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)0.000
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.289
Teacher spread0.246 · 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

Citations11
Published2002
Admission routes2
Has abstractyes

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