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Record W2004651383 · doi:10.1109/tpwrs.2014.2314717

Co-Ordinated PSS Tuning of Large Power Systems by Combining Transfer Function-Eigenfunction Analysis (TFEA), Optimization, and Eigenvalue Sensitivity

2014· article· en· W2004651383 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 Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMcGill University
Fundersnot available
KeywordsEigenfunctionTransfer functionEigenvalues and eigenvectorsSensitivity (control systems)ComputationElectric power systemControl theory (sociology)Mathematical optimizationMathematicsFunction (biology)Power (physics)Computer scienceApplied mathematicsEngineeringAlgorithmElectronic engineeringPhysics

Abstract

fetched live from OpenAlex

This paper shows that a combination of: 1) the computation time-saving transfer function and eigenfunction analysis (TFEA) method, 2) eigenvalue sensitivity concept, and 3) optimization techniques makes a powerful tool in coordinated tuning of power system stabilizers (PSS). The combination allows different PSS tuning strategies to be evaluated as objective functions of optimization under constraints. Feasibility of the method is demonstrated by numerical results from a 69-generator system. Two objective functions are used as illustrative examples.

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)0.001
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 score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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