Bibliographic record
Abstract
Increased load demand can severely deteriorate the performance of a power system. Reactive compensation allocation is a common method to allow a power system to return to an acceptable performance level for an expected load increase. The reactive power planning problem (RPP) is used to determine the optimal placement of reactive devices for a set of objectives. The RPP is a large scale, multi-objective, highly constrained and partially discrete optimization problem that is very difficult to solve. -- Heuristic optimization techniques have been used as a means to solve difficult optimization problems including many power system optimization problems. Heuristic techniques based on evolutionary strategies have been used to solve RPPs as they overcome many of the difficulties with classical optimization techniques. However, new multi-objective evolutionary computational techniques have shown the ability to consider an optimization problem's objectives independently for the determination of Pareto-optimal solutions. -- A popular multi-objective evolutionary strategy called the Non-Dominated Sorting Genetic Algorithm II (NSGAII) is applied to a series of multi-objective RPP case studies in this research. The results from the case studies presented show that the tool is able to determine feasible, non-dominated V Ar source allocation schemes that allow a system to operate safely under an assumed load growth.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".