On the Assessment of Sampling Rate Impacts on Responses of Digital Protective Relays
Bibliographic record
Abstract
Power systems (including industrial and commercial power systems) widely utilize digital protective relays for component and system protection. These protective devices have shown numerous operational advantages over conventional electromagnetic protective devices. Such operational advantages include the accuracy, reliability, response speed, interoperability, weight, and size. Performance and operational advantages of digital protective relays are typically dependent on the resolution of their input data, as well as their algorithms for fault detection and identification. This article assesses the performance of time-based, frequency-based, and time-frequency-based digital protective relays, when operated at different sampling rates. Tested sampling rates include low, medium, and high sampling rates that range from 16 to 288 samples per cycle. In this article, the three digital protective relays are tested when deployed as the digital 87 T transformer digital protection. Performance of the time-based, frequency-based, and time-frequency-based digital protective relays is assessed in terms of their accuracy and response speed. Test results show that low sampling rates can deteriorate the accuracy and response speed of the three tested digital protective relays. Obtained performance results also reveal that medium and high sampling rates can effectively improve the accuracy and response speed of the time-based, frequency-based, and time-frequency-based digital protective relays.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".