Multi‐objective design of advanced power distribution networks using restricted‐population‐based multi‐objective seeker‐optimisation‐algorithm and fuzzy‐operator
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it