MOSOA-Based Multiobjective Design of Power Distribution Systems
Bibliographic record
Abstract
This paper presents a multiobjective (MO) evolutionary algorithm for solving a contingency-based MO design of power distribution system (PDS) by extending the original and powerful metaheuristic approach based on a MO seeker optimization algorithm (MOSOA). Normally, reliability is a major concern in existing PDS planning, as estimation of failure rates and fault repair duration of the feeder branches is difficult in practice. The proposed planning methodology uses a contingency-load-loss index for reliability evaluation, which is independent of the failure rate and fault repair duration of the feeder branches. This planning strategy includes distribution automation devices such as automatic reclosers (RAs) to enhance the reliability and efficiency of the distribution system. The proposed algorithm generates a set of nondominated solutions by the simultaneous optimization of two conflicting objectives (economic cost and overall system reliability) using Pareto-optimality-based tradeoff analysis. The performance of the proposed approach is assessed and illustrated on a 54-bus distribution system, considering real-time design practices and meeting the additional requirements that the designer imposes. The information gained from the Pareto-optimal solution is shown to be useful for final decision making of a PDS. Furthermore, a qualitative comparison is made with the nondominated sorting genetic algorithm-II, showing the efficacy of the proposed planning approach.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".