Optimización del uso de la potencia reactiva en el sistema eléctrico ecuatoriano mediante la programación no lineal
Bibliographic record
Abstract
In recent years mainly due to factors such as increased consumption of electricity in cargo areas have led to the electric system to work closer to their limits, producing significant changes in reactive power flows in transmission lines constitute one of the causes associated with the instability of the power system [1]. Insufficient or poor resource allocation of the reactive power in a power grid leads to voltage drops in the load centers, limiting the ability to transfer real transmission systems, leading to problems of voltage instability and voltage collapse risk [2]. Countries like Japan, France, Canada and the USA have reported cases of voltage collapse with millions losses [3]. To avoid these cases, system operators and researchers are looking for methods that can improve the optimal scheduling of reactive power resources considering minimizing power losses in the transmission lines and the constraints associated with the operation of the system. This research project will seek to minimize power losses in transmission lines, resulting reactive power contributed by each element of the power system in order to achieve a reliable, safe and optimal operation, the effect is to use a software called “General Algebraic Modeling System-GAMS which solve the optimization problem. GAMS modeling system is a high-level mathematical programming and optimization, consists of a language compiler and integrated high performance status [4]. GAMS is designed for complex and large-scale modeling applications, allowing to build large models that can quickly adapt to new situations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".