Multi‐objective design of advanced power distribution networks using restricted‐population‐based multi‐objective seeker‐optimisation‐algorithm and fuzzy‐operator
Bibliographic record
Abstract
This study proposes a method for designing advanced power distribution system (PDS) including distributed generations, using a combination of fundamental loop generator and multi‐objective seeker‐optimisation algorithm (MOSOA). The proposed approach reduces the searching space using fundamental loop generator technique to obtain initial feasible solutions which is further improved by SOA to generate new set of solutions with improved aptitude. The proposed methodology uses a contingency‐load‐loss‐index for reliability evaluation, which is independent of the estimation of failure rate and fault repair duration of feeder branches. This planning strategy includes distribution automation devices such as automatic reclosers (RAs) to enhance the reliability of PDS. The proposed algorithm generates a set of non‐dominated solution by simultaneous optimisation of two conflicting objectives (economic cost and system reliability) using Pareto‐optimality‐based trade‐off analysis including a fuzzy‐operation to automatically select the most suitable solution over the Pareto‐front. The performance of the proposed approach is assessed and illustrated on 54‐bus and 100‐bus PDS, considering realtime design practices. Extensive comparisons are made against some well‐known and efficient MO algorithms such as fast non‐dominated sorting genetic algorithm‐II, MO particle‐swarm‐optimisation and MO immunised‐particleswarm‐optimisation. Simulation results show that the proposed approach is accurate and efficient, and a potential candidate for large‐scale PDS planning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".