Adaptive overcurrent relay (AOCR) based on Fano Factors of current signals
Bibliographic record
Abstract
Abstract A Fano Factor‐based adaptive overcurrent protection scheme, that automatically adjusts the protection settings in response to the prevailing conditions of the power system, is presented. Both phase and ground overcurrent relays are based on the same mathematical algorithm to calculate the operating time using Fano Factors (FFs) estimated for phase and ground current signals, respectively. Effectiveness of the proposed adaptive overcurrent method is validated for various fault types under a plethora of loading current levels, besides a wide range of fault inception angles and fault resistance. Simulation results confirm that the proposed algorithm can detect series and shunt faults, differentiate between grounding and phase faults, and adapt the relay operating time settings using the FF for all conditions examined. The relay algorithm speed and sensitivity are controllable using both the FF setting values and moving data window size. Moreover, it is characterized by being simple, reliable, accurate, and can be implemented practically as a base of digital protective relay for use in substation automation systems and phasor measurement units.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".