Bibliographic record
Abstract
The author proposes an effective capacitor control model for unbalanced radial and meshed distribution systems. Due to the linear characteristic of the proposed model, a constant sensitivity matrix relating the incremental capacitor shifts and system status can be derived, and then the sensitivity-based objective function and network constraints can be obtained. The linear formulation can be solved by the commonly used linear programming and integer programming techniques, which are among the best choices for real-time control in terms of computational speed, reliability, and ability to handle many different operating constraints. The development of the sensitivity matrix does not need any assumptions about voltage magnitudes, voltage angles, line r/x ratios, and network topology; thus, the proposed method can achieve high robustness and accuracy. The proposed capacitor control model can be used to solve the capacitor placement and real-time capacitor control problems; however, in order to verify the accuracy of the model, only the corrective dispatching problem is solved in this work. Test cases including the unbalanced radial and meshed distribution systems and a large-scale distribution system acquired from Taiwan Power Company are all conducted. Test results show that the proposed method can effectively handle the capacitor control problems and has great potential to be integrated into distribution automation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".