Event Signatures in H-PMU Measurements: An Information-Theoretic Analysis of Real-World Data
Bibliographic record
Abstract
A harmonic phasor measurement unit (H-PMU) is a technological evolution of the conventional PMU. Unlike a conventional PMU that solely captures fundamental phasors, an H-PMU encompasses the measurements of both fundamental and harmonic phasors. So far, the application of H-PMU measurements has been on the analysis of steady-state characteristics of harmonic phasors, such as for harmonic source identification or harmonic state estimation. However, in this paper, we take a rather unique and multi-disciplinary approach to harness the additional information provided by harmonic phasor signatures to better analyze power system events. The proposed approach is data-driven and from the view point of information theory, and based on real-world H-PMU measurements. Our analysis reveals the presence of significant independent information content in the extracted features from the event signatures in harmonic phasor measurements. This study also explores the applications of utilizing such additional information content, such as to optimally select the orders of the harmonic phasors for the analysis of power system events, as well as to enhance the performance in the task of event clustering in power systems situational awareness.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".