Optimización del uso de la potencia reactiva en el sistema eléctrico ecuatoriano mediante la programación no lineal
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.
Bibliographic record
Abstract
In recent years mainly due to factors such as increased consumption of electricity in \ncargo areas have led to the electric system to work closer to their limits, producing \nsignificant changes in reactive power flows in transmission lines constitute one of the \ncauses associated with the instability of the power system [1]. \nInsufficient or poor resource allocation of the reactive power in a power grid leads to \nvoltage drops in the load centers, limiting the ability to transfer real transmission \nsystems, leading to problems of voltage instability and voltage collapse risk [2]. \nCountries like Japan, France, Canada and the USA have reported cases of voltage \ncollapse with millions losses [3]. To avoid these cases, system operators and \nresearchers are looking for methods that can improve the optimal scheduling of \nreactive power resources considering minimizing power losses in the transmission \nlines and the constraints associated with the operation of the system. \nThis research project will seek to minimize power losses in transmission lines, \nresulting reactive power contributed by each element of the power system in order to \nachieve a reliable, safe and optimal operation, the effect is to use a software called \n“General Algebraic Modeling System-GAMS" which solve the optimization \nproblem. \nGAMS modeling system is a high-level mathematical programming and \noptimization, consists of a language compiler and integrated high performance status \n[4]. GAMS is designed for complex and large-scale modeling applications, allowing \nto build large models that can quickly adapt to new situations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it