Optimal Design of Distribution Overhead Powerlines Using Genetic Algorithms
Bibliographic record
Abstract
As the design of distribution overhead power lines becomes increasingly standardized in the 21st century, the reduced search space of its design parameters allows for increasing opportunities to automate the design process. It involves specifying the distribution utility pole heights and classes, pole-top attachments, and conductor span tensions. A successful algorithm must be able of generating designs that are compliant with applicable codes and utility standards, achieve construction labour and material costs that are commensurate to that of a human-created design, and require a reasonable computation time. A design automation algorithm is created for ATCO Electricity, Alberta, Canada. Based on provided requirements and constraints, it uses the genetic algorithm to optimize the design parameters, including a completed design staking list, material loading file, and relevant calculation reports for use by the design department. Evaluation of the developed tool is based on five samples of real design scenarios. Within a reasonable amount of computation time, the tool produces results free from major design errors, comparable in material and construction costs to those of human-created designs. Furthermore, the design automation tool makes rigorous use of design decisions that result in small cost savings but that are not commonly found in human-created designs.
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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) | 0.001 | 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 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".