A Novel Zooming Algorithm for Distribution Load Flow Analysis for Smart Grid
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Bibliographic record
Abstract
In this paper, a novel algorithm for calculating distribution load flow (DLF) for smart grid is presented. The algorithm can provide a DLF solution in a specific area of interest in the distribution system without the necessity of including all of the distribution system laterals in the DLF problem. The system laterals excluded from the DLF solution are replaced by equivalent loads using a proposed lumping technique. The proposed DLF zooming method thus reduces the size of the DLF problem and hence minimizes the effect of system size on the DLF solution time. The construction of the DLF problem is based on a novel automated recursive process, which enables fast accommodation of changes in the network topology of smart grid. The simulation results validate the accuracy of the proposed DLF zooming algorithm and provide a favorable evaluation of its performance for smart grid operation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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