An Enhanced Algorithm for Conventional Protection Devices of Transmission Lines With DTLR
Bibliographic record
Abstract
As the installed capacity of renewable generation continues to increase nowadays to satisfy both emissions and economic requirements, the nature of intermittent generation results in a need to further increase transmission capacity. Dynamic thermal line rating may be used to increase the capacity of transmission lines without upgrading infrastructure. However, existing transmission line protection devices may mal-operate due to the increased capacity. So, it is required to evaluate the performance of existing protection devices. This paper proposes an algorithm that enhances the performance of existing conventional relays such as distance and overcurrent relays. The proposed algorithm is based on a combination of dynamic thermal rating, current ratio of negative and positive sequences and voltage criterion to detect faults whether symmetrical or asymmetrical and unsafe overload. In addition, it aims at restraining the existing relays during safe overloads. A model of a system under investigation is simulated, with studies are performed in MATLAB/Simulink environment. The results demonstrate the ability of the proposed scheme to detect all types of faults and unsafe overload dependably and restrain the conventional relays securely during safe overloads.
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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 | 0.001 | 0.000 |
| 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) | 0.005 | 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".