Spectral Continuity and Subspace Change Detection for Recovery of Missing Harmonic Features in Power Quality
Bibliographic record
Abstract
The disturbance monitoring, event sequence record ing, and automated fault analysis in the power system require processing of power quality and digital fault recorder data. Through, reliable and granular data streaming/storage services, which inadvertently introduces unwanted data quality issues like data gaps or missing samples. The work proposes a data-driven, gap length, and spectral change independent missing harmonic recovery method using static and dynamic selectivity criteria of spectral continuity and rate of subspace affinity change. For static selectivity, a novel mean-shift cross-energy operator is proposed that quantifies the spectral similarity between the static snapshots of signals across the gap. For dynamic selectivity, a novel rate of subspace change method is proposed to detect the subspace change points in a dynamically changing data set. Based on the selectivity criterion, the missing harmonic parameters are estimated and filled using the rotational invariance technique. The proposed method could effectively reconstruct the power signals with longer data gaps, contiguous, and randomly gaped data sets under dynamic harmonic conditions. The proposition is tested with simulated data sets in Matlab/Simulink and real system data from the India grid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".