Optimal Coordination of Directional Overcurrent Relays Using BBO when Electromechanical, Static, Digital, and Numerical Relays All Exist
Bibliographic record
Abstract
Nowadays, the literature is very rich with many techniques proposed especially for optimal relay coordination (ORC) problems of directional overcurrent relays (DOCRs). Many approaches have been applied to solve this stiff problem by considering different scenarios on protective relays and their network. However, the literature lacks a realistic model to deal with the inevitable fact that modern electric power networks have different relay technologies. Protection engineers could face electromechanical, static, digital “hardware-based”, and numerical “software-based” relays all together in the same network. Thus, existing ORC solvers are inapplicable to optimally coordinate such realistic networks. This paper presents a corrected model to deal with different relay technologies where the original problem dimension increases by 1.5× to indicate the relay types. To validate its correctness, the biogeography-based optimization (BBO) algorithm is used with the IEEE 6-bus, 15-bus, and 42-bus test systems. The results show that this realistic ORC problem can be solved, which means that it is possible to coordinate DOCRs that come with different speeds, coordination time intervals (CTI), and resolutions of time multiplier settings (TMS) and pickup settings (PS).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".