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Record W4239626846 · doi:10.1049/iet-gtd.2017.0345

Solution techniques for transient stability‐constrained optimal power flow – Part I

2017· article· en· W4239626846 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

VenueIET Generation Transmission & Distribution · 2017
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of AlbertaCarleton University
FundersU.S. Department of Energy
KeywordsTransient (computer programming)Power flowStability (learning theory)Control theory (sociology)Flow (mathematics)Transient flowComputer scienceElectric power systemPower (physics)Mathematical optimizationMathematicsEngineeringMechanicsElectrical engineeringPhysicsThermodynamicsSurgeArtificial intelligence

Abstract

fetched live from OpenAlex

This series of studies present the state‐of‐the‐art for the solution of the transient stability constrained optimal power flow problem (TSC‐OPF). Three different classes of solution techniques: dynamic optimisation‐based, SIME method, and computational intelligence, are discussed in detail. Moreover, discussed are issues to consider while solving such problems, various application areas, and future directions in this research area. A comprehensive resource of the available literature, publicly available test systems, and relevant numerical libraries is also provided. This study presents the TSC‐OPF formulation and discusses various dynamic optimisation‐based approaches. Two optimisation techniques, full‐space and reduced‐space method, are presented for solving the resulting non‐linear optimisation problem.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.250
Teacher spread0.224 · 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