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Record W1992485468 · doi:10.1109/tpwrs.2012.2205279

Estimation of Electromechanical Modes of Power Systems by Transfer Function and Eigenfunction Analysis

2012· article· en· W1992485468 on OpenAlexaff
Reza Jalayer, Boon‐Teck Ooi

Bibliographic record

VenueIEEE Transactions on Power Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMcGill University
Fundersnot available
KeywordsEigenvalues and eigenvectorsEigenfunctionComputationAlgorithmComputer scienceApplied mathematicsMathematicsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

This paper presents a method of estimating the electromechanical damping and frequency coefficients of interconnected power systems. The method combines AESOPS approach and eigenvalue analysis. Computation time is reduced by applying an approximation based on an eigenvalue sensitivity formula, which evaluates eigenvectors of the 2ng× 2ng[A(ωK)] and [A(ωK)]Tmatrix and thereafter use them to obtain improved estimates of the eigenvalues. Numerical results from a 16-generator system demonstrate the feasibility of the proposed method.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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