Synchrophasor data analytics in distribution grids
Bibliographic record
Abstract
The deployment of high-fidelity, high-resolution sensors in distribution systems will play a key role in enabling increased resiliency and reliability in the face of a changing generation landscape. In order to leverage the full potential of such a rich dataset, it is necessary to develop an analytics framework capable of both detecting and analyzing patterns within events of interest. This work details the foundation of such an infrastructure. Here, we present an algorithm for detecting events, in the form of edges in voltage magnitude time series data, and an approach for clustering sets of events to reveal unique features that distinguish different events from one another (e.g. capacitor bank switching from transformer tap changes). We test the proposed infrastructure on distribution synchrophasor data obtained from a utility in California over a one week period. Our results indicate that event detection and clustering of archived data reveals features unique to the operation of voltage regulation equipment. The chosen data set particularly highlights the value of the derivative of the localized voltage angle as a distinguishing feature.
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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".