Fuzzy optimal capacitor sizing and placement
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
The installation of power capacitors in distribution systems yields numerous economical benefits and improvements in system performance. There are many algorithms to determine the optimal capacitor sizes and their placement in distribution systems. All of these methods require data, which may be uncertain in nature, to generate an acceptable solution to the capacitor sizing and placement problem. To account for such uncertainties in these parameters, a new method using fuzzy set theory is proposed. This proposed method employs a known method for optimal capacitor sizing and placement but models its parameters by possibility distribution functions. The generated fuzzy solutions offer a more suitable means of evaluating the goodness of the results based on uncertain data. In this paper, a fuzzy optimal capacitor sizing and placement method is applied to a practical test case 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 | 0.000 |
| 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