Optimal coordination of directional overcurrent relays using hybrid BBO/DE algorithm and considering double primary relays strategy
Bibliographic record
Abstract
Finding optimal settings of directional overcurrent relays (DOCRs) has been extensively solved by many scenarios with adopting different traditional and modern optimization algorithms. For the sack of simplicity, all the conducted studies in the literature are restricted on a condition that all DOCRs are numerical, static, or electromechanical. There is a fact that when jumping from electromechanical or static relays to numerical relays the former devices could be used as a second wall of protection instead of just throwing them. With a very extreme condition, which may not happen in the real-world applications, all buses will have double primary DOCRs, which where act as backup DOCRs for other buses. That is, the dimension of any given problem is duplicated and becomes very hard to be feasibly and optimally solved. This paper covers this uncommon scenario, and a new hybrid algorithm with some additional sub-algorithms is developed to solve the IEEE 6-bus test system. Moreover, both numerical with electromechanical DOCRs and numerical with static DOCRs are considered in this study.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".