Bibliographic record
Abstract
Phasor estimation algorithms for protective relaying are required to filter out unwanted components from the input signals and retain only the components of interest. The components to be removed include harmonics and the decaying-exponential transient (dc offset) component. They affect the accuracy and the speed of convergence of the phasor estimation algorithms to a great extent. This paper presents a new technique, which effectively removes the harmonics and the decaying dc component present in the input signals, within half a cycle of the power system frequency. This is achieved by means of a simple computational procedure using three off-line look-up tables. The proposed algorithm has been tested for a wide variety of signals to assess its performance. The performance is also compared with the two most popular half-cycle phasor estimation algorithms; the half-cycle least error squares algorithms and the mimic plus half-cycle Fourier algorithm. The test results show that the proposed relaying algorithm has a faster convergence and better accuracy compared to these previously proposed algorithms. The results also indicate that the proposed algorithm converges to its final value within half a cycle of the power system frequency as compared to the other two algorithms, which take more than half a cycle to converge, when a decaying dc component is present in the input.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".