Optimal coordination of directional overcurrent relays using hybrid BBO-LP algorithm with the best extracted time-current characteristic curve
Bibliographic record
Abstract
The coordination problem of directional overcurrent relays (DOCRs) is considered as a highly constrained, nonlinear, and non-convex optimization problem. The summation of the operating times of all DOCRs, when they act as primary protective devices, is taken as the objective function that needs to be minimized. This stiff problem is mostly optimized based on IEC standard inverse time-current characteristic curve (TCCC) and using discrete plug setting (PS) to simulate electromechanical DOCRs. From the literature, some few papers have solved this coordination problem by using different TCCCs. However, this approach increases the problem dimension by 250%, which in turn consumes more CPU time and needs more iterations for converging to near-optimal solutions. Moreover, coordinating DOCRs with different TCCCs could violate the selectivity criteria in some unconsidered fault locations, because satisfying the optimality at the near-end 3φ faults does not guarantee the feasibility of other fault locations. This paper solves all these points by heuristically selecting the best TCCC among a large variety of North American and European standard TCCCs. In addition, this paper utilizes the advanced features available in modern numerical relays to obtain new solutions based on continuous PS. The performance of the proposed BBO-LP optimization technique is evaluated using a 15-bus system.
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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) | 0.002 | 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 | 0.001 |
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".