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Fluxo de potencia otimo parametrico

2000· article· es· W2224802780 on OpenAlexaboutno aff
Flavio Guilherme de Melo Lima

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Este trabalho apresenta um metodo parametrico de resolucao do problema de Fluxo de Potencia Otimo (FPO) para sistemas de potencia de grande porte. O trabalho e uma continuacao das pesquisas anteriores em otimizacao parametrica aplicada no FPO desenvolvidas na UNICAMP e na Universidade McGiII, em Montreal, Canada. A abordagem parametrica consiste em relaxar o problema original (FPO) atraves da incorporacao de termos parametricos na funcao objetivo e nas restricoes de igualdade e desigualdade dando surgimento ao problema relaxado (Fluxo de Potencia Otimo Parametrico­FPOP). A relaxacao do FPO assegura que qualquer solucao inicial arbitraria, factivel ou nao no problema original, seja solucao otima do FPO relaxado. Atraves da variacao de um parâmetro, uma familia de problemas parametricos e resolvida pelo metodo de Newton-Raphson, cujas solucoes formam um caminho que parte do problema relaxado indo ate a solucao do problema original. Uma estrategia eficiente para a determinacao do conjunto de restricoes de desigualdade ativas de cada problema parametrico foi desenvolvida. O metodo foi testado em duas versoes do sistema eletrico brasileiro Sul-Sudeste, uma contendo 810 barras e uma outra com 2256 barras na qual esta representada a interligacao do Sul-Sudeste com o Norte-Nordeste. Foram considerados os problemas de minimizacao do custo de geracao, minimizacao de perdas ativas e minimizacao de desvio de tensao. Os resultados mostraram que a abordagem parametrica e uma tecnica robusta e eficiente de resolucao de problemas de FPO em sistemas de grande porte Abstract

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 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 score0.830
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.003
GPT teacher head0.192
Teacher spread0.189 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2000
Admission routes1
Has abstractyes

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