A fuzzy logic framework for control of switched capacitors in distribution systems
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Bibliographic record
Abstract
This paper proposes a fuzzy expert system for the multilevel control of switched capacitors installed on a distribution system with a nonconforming load profile. The control objectives are minimization of power system losses without violating the voltage security of the power system. Expert systems enhanced by fuzzy sets are used to determine the control variables corresponding to the given load values. The rules are adapted using a neural learner to build the rule set and train the membership functions. A load flow determines the corresponding state of the power system. The knowledge base chooses the design from a set of suboptimal solutions obtained from the load flow. The method is based on the application of fuzzy sets to sensitivities in expert systems to refine the solution. Initial trial runs using the above approach on a 30-bus distribution system are very encouraging. Simplicity, processing speed and ability to model load uncertainities make this approach a viable option for online VAr control.
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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