Optimal configuration of underground cables to maximise total ampacity considering current harmonics
Bibliographic record
Abstract
This study presents an efficient algorithm to find optimal underground cable configuration with maximum ampacity in a concrete duct bank. The current's harmonics and its effects on the sheath losses are considered in the proposed algorithm. To find the optimal configuration of the cables in the duct bank, two heuristic optimisation methods are applied: the first algorithm is the well‐known particle swarm optimisation (PSO), and the second one is the shuffled frog‐leaping algorithm (SFLA) which has attracted considerable attraction in recent years. The objective function of these methods which has to be optimised is total ampacity. Calculating the total ampacity for a required configuration by using PSO/SFLA is a convex optimisation problem. The interior point method is utilised to solve this problem. The proposed method has been implemented on four test cases to show the importance of considering the current harmonics in determining the optimal configuration. To evaluate the performance of the PSO and the SFLA, the obtained results are compared in different test cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".