A Novel Architecture and Algorithm for Adaptive Synchrophasor Estimation in Renewable-Rich Electrical Distribution System
Bibliographic record
Abstract
Sensing and measurement devices are keeping pace with the advancement in the industrial power distribution system. The ability to provide time-synchronized measurements at a fast reporting rate by distribution-level PMUs (D-PMUs) specially with increasing distributed energy resources (DERs) offer great opportunities for monitoring and control. However, unlike the transmission systems, the distribution system waveforms typically have more noise, harmonics and unbalanced phases, posing unique challenges to estimate phasors at the distribution-level. Lack of specific standards for performance requirements of D-PMUs make this further challenging. This work proposes a novel smart synchrophasor device architecture for estimating phasors on polluted signals. The proposed sensor architecture is adaptive to varying system conditions and can adjust reporting rates based on system demands. The proposed approach employs a Sliding Fast Fourier Transform (SFFT) and Signal Estimation by Minimizing Parameter Residuals (SEMPR) technique to simultaneously estimate the harmonic components along with the fundamental phasor. Further, to accommodate the signals generated from varying system conditions in the distribution system, an approach is proposed to update the measurement model for the PMU estimation using adaptive filtering and goodness-of-fit (GoF) measure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".