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Record W2106652091 · doi:10.1109/59.871729

Optimal power flow by a nonlinear complementarity method

2000· article· en· W2106652091 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 · 2000
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNonlinear systemPower flowNonlinear complementarity problemMathematicsNewton's methodComplementarity (molecular biology)Complementarity theoryMathematical optimizationApplied mathematicsMathematical analysisControl theory (sociology)Electric power systemPower (physics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

A nonlinear complementarity method for solving nonlinear optimal power flow problems is presented. This method stems from proposed reformulation of complementarity problems as nonlinear systems of equations which age, in turn, solved by a Newton-type method. To reformulate optimal power flow problems as nonlinear systems of equations we employ a function /spl psi//sub /spl mu//: /spl Rscr//sup 2//spl rarr//spl Rscr/ that satisfies the property /spl psi//sub /spl mu//(a,b)=0/spl hArr/a>0, b>0 and ab=/spl mu/, for any /spl mu/>0. Then, unlike interior-point methods, the new method handles the complementarity conditions for optimality, s/sub i//spl ges/0, /spl pi//sub i///spl ges/0 and s/sub i//spl pi//sub i/=0, without requiring that s/sub i/>0 and /spl pi//sub i/>0 be satisfied at every iterate. Numerical results illustrate the viability of the proposed method as applied to several power networks. A comparison with two interior-point algorithms is discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.964
Threshold uncertainty score1.000

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)0.0040.001

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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