Effects of Sampled-Values Data Quality on Responses of Time-Frequency Based Digital Relays
Bibliographic record
Abstract
Time-frequency based digital protective relays can offer several advantages of high accuracy, improved reliability, and fast response over other protective relays. These protective relays employ half-band digital finite impulse response (FIR) filters to process currents and/or voltages to extract fault signatures, which facilitate accurate detection and identification of fault events. Coefficients of the employed half-band digital FIR filters are determined by wavelet or phaselet functions. The signal processing part in a time-frequency based digital protective relay processes samples of measured currents and/or voltages. As a result, attention has to be paid to the measurement, digitization, and communication of current and voltage data. Power systems utilize the IEC 61850 standard to implement data communication between measurement, control, and protection devices. This article assesses the impacts of the IEC 61850 sampled values on the accuracy and response speed of time-frequency based digital protective relays. The presented assessment is conducted for two sample cases; the transformer differential protection and arcing current fault detection and identification. Test results show that the accuracy and response speed of time-frequency based digital protective relays are maintained, when measured currents and voltages are communicated using the IEC 61850 sampled values.
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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 | 0.004 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".