Bibliographic record
Abstract
The availability of an adequate amount of electricity and its utilization is essential for the growth and development of the country.Development of distribution systems result in higher system losses and poor voltage regulation.Consequently, an efficient and effective distribution system has become more urgent and important.Hence proper selection of conductors in the distribution system is important as it determines the current density and the resistance of the line.This paper presents optimal branch conductor selection of radial distribution systems using intelligent method of Genetic Algorithm (GA) with the objective to minimize the overall cost of annual energy losses and depreciation on the cost of conductors in order to improve productivity.The back/forward sweep method is applied for load flow solution of proposed radial distribution system.Simulations are carried out on 69-bus radial distribution networks based on GA optimization method.Results obtained with the proposed approaches are compared.The power loss reduction and voltage profile improvement has been successfully achieved which demonstrate the effectiveness of the proposed methods.
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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) | 0.995 | 0.979 |
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; both teacher heads agree on what is shown here.
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".