HEURISTIC JUSTIFICATION AND DIFFERENTIAL EVOLUTION-BASED FINAL SELF-RESTORATION STATE OPTIMIZATION FOR URBAN POWER GRID AFTER BLACKOUT
Bibliographic record
Abstract
Urban power grid has to be self-restored with a lack of power sources after blackout while waiting for external power supply. An optimal target system of self-restoration can instruct engineers or expert system to make an approximately optimal restoration plan. In this paper, a problem named final self-restoration state optimization (FSRSO) is put forward, and a simplified mathematical model of FSRSO is presented with the skeleton of power grid that can be given by engineers. A heuristic justification (HJ) is proposed to be integrated with differential evolution (DE) algorithm to solve the problem. HJ adjusts the active power output of the slack bus to be within limits, decreases constraints violations with great probability and therefore speeds up the evolution procedure of DE. Losses ratios of individuals of the last generation are utilized by HJ to evaluate power losses. According to tests results on IEEE 39-bus system, DE integrated with HJ gets better solution in much less iterations than the classical DE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".