Optimal Coordination of Double Primary Directional Overcurrent Relays Using a New Combinational BBO/DE Algorithm
Bibliographic record
Abstract
The optimal relay coordination (ORC) problem for directional overcurrent relays (DOCRs) has been solved by many conventional and modern optimization algorithms. All these studies were based on the common assumption that all DOCRs are numerical, digital “hardware-based,” static, or electromechanical. Unfortunately, the transition between these technologies does not happen instantaneously, so protection engineers could see different models of these protective relays in some real electric power systems. Moreover, when old electromechanical, static, and digital relays are replaced with the latest state-of-the-art numerical relays, the older relays are kept as backup protective devices. Some called them “primary” and “local-backup” relays, whereas others called them “main-1” and “main-2” relays. The reason behind the second terminology is the chance that the old relays could act ahead of the numerical relays. In this paper, a realistic mathematical model of the ORC problem is formulated and solved using a new hybrid evolutionary algorithm. To judge whether this realistic ORC problem is completely/partially solvable or not, the IEEE 6-bus, 15-bus, and 42-bus test systems are simulated. The results prove that the technique is an effective tool to indicate which relay sets accept/do not accept this double primary relay strategy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".