A New Distribution System Performance Approach to the Switch Allocation Problem Under Smart Grid Framework
Bibliographic record
Abstract
This paper proposes a new vision of the Distribution System Switch Allocation problem considering new system performance measures. The mathematical model has been rebuilt with a new aggregated multi-objective formula, minimizing a newly developed performance index while achieving minimum annual energy lost. A new practical weighted combined system performance index, consisting of Reliability, Resiliency and Vulnerability, is applied and tested to be used by utilities replacing the common simple reliability index combination. The new model uses mixed integer design variables to determine the number, location and status of switches. A set of eight logical and technical constraints was applied to provide the best description of the real existing system constraints. A new algorithm of checking the system radial topology is also applied to the problem. The problem was solved using the Genetic Algorithm and was tested on a 54-bus real distribution test system, deemed more complicated than the test systems found in literature, to demonstrate its validity and effectiveness in real life systems.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".