A new filtering technique to eliminate decaying DC and harmonics for power system phasor estimation
Bibliographic record
Abstract
During faults, the voltage and current signals available to the relay are affected by the decaying DC component and harmonics. To make proper decisions, most of the relaying algorithms require the fundamental frequency phasor information immune to decaying DC effect and harmonics. Conventional Fourier and LES phasor estimation algorithms are affected by the presence of decaying-exponential transient in the fault signal. This paper presents modified Fourier algorithm, which effectively eliminates the decaying DC component and the harmonics present in the fault signal. The decaying DC parameters are estimated by means of an out-of-band filtering technique. The decaying DC offset and harmonics are removed by means of a simple computational procedure which involves designing of two sets of orthogonal digital DFT filters tuned at different frequencies and by creating three off-line look-up tables. The technique was tested for different decay rates of the decaying DC component. The proposed technique is compared with the conventional mimic plus full cycle DFT algorithm. The results show that the proposed technique has a faster convergence to the desired value compared to the conventional mimic plus DFT algorithms over a wide range of decay rates. In all the cases the convergence to the desired value is achieved within one cycle of the power system frequency
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".