Fuzzy EP algorithm and dynamic data structure for optimal capacitor allocation in radial distribution systems
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Bibliographic record
Abstract
Optimal reactive-power compensation in a radial distribution system requires the determination of the best set of locations for siting capacitors of minimum sizes. The total cost of compensation should be the least and must yield the maximum energy-loss reduction accounting for various load levels. Other controls such as transformer taps, reconfiguration options and existing reactive-power sources must be considered while searching for the optimal solution. Optimal selection of a few best sites from among a large set is a problem of high combinatorial order and difficult to solve using conventional optimisation techniques. Optimal sizing is a problem of a continuous nature. The paper proposes a single dynamic data structure for an evolutionary programming (EP) algorithm that handles the problems of siting and sizing of new shunt capacitors simultaneously while considering transformer taps, existing reactive-power sources and reconfiguration options, accounting for different load levels and time durations. A fuzzy model of the objective function is developed for optimisation in the EP framework. The proposed fuzzy EP method is tested on two cases of a 69-bus radial distribution system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it