Service restoration in balanced and unbalanced distribution systems with high DG penetration
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
This paper presents a simple yet efficient service restoration algorithm for balanced and unbalanced radial distribution networks including Distributed Generators DG. The proposed algorithm that is based on load flow, begins with identifying the de-energized loads group due to fault isolation. All tie switches connecting this group with the rest of the network are the candidate switches for restoration. Branch currents from meshed network (i.e. closing all tie switches) were used as switching index to determine which one of these candidate tie switches should be closed for restoration. The restoration is carried out with objectives of maximizing the restored loads and minimizing the number of switching operations under line current limits, bus voltage limits and radial topology constraints. Each out-of-service group restored by closing the candidate tie switch with highest switching index. The proposed method has been tested on one balanced and two unbalanced systems with and without distributed generation, and compared with those reported in the literature.
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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