Processing synchrophasor data using a feature selection procedure
Bibliographic record
Abstract
Wide deployment of synchrophasor technology revolutionizes power grid operation. Phasor Measurement Units (PMU) record synchrophasor data at a tremendous scale, how to process and interpret such data in most effective way and extract useful parameters for a particular application is a main challenge. In this paper, a procedure called “feature selection” is proposed for synchrophasor data processing, which is paramount to the success of nearly all synchrophasor applications. The proposed procedure is utilized in a case study for fault detection. Although creating a rule-based fault detection technique is the ultimate goal, pre-processing of synchrophasor data is the first critical step to explore system responses during fault events, and demonstrate underlying relationship among different parameters before rules can be established. In this case study, synchrophasor data measured from a large power grid are processed, key parameters are first chosen based on the feature selection procedure, these parameters are then calculated and/or demonstrated through tables and graphs.
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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 | 0.001 |
| 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".