Ensemble decision trees for phasor measurement unit-based wide-area security assessment in the operations time frame
Why this work is in the frame
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Bibliographic record
Abstract
This study proposes ensemble decision trees for phasor measurement units (PMUs)-based wide-area security assessment to provide early warnings of deteriorating system conditions. In the proposed technique, the wide-area response signals in real-time operation are captured after 1 and 2 s fault clearing time, from the respective monitoring buses where PMUs are placed. These wide-area post-disturbance records are processed in time and frequency domains for extracting selected decision features such as the peak spectral density of the angle, frequency and their dot product evaluated over the grid areas called as wide-area severity indices (WASI). WASI are used as input features to train the random forests (RFs) to build effective predictor for early warnings in security assessment. The RF-based learning not only provides high performance accuracy but is also effective in valuing the importance of, and the interaction among, the various WASI input features, for developing the reliable predictor. The RF has been successfully tested for classifying both system-wise and area-wise NERC-compliant contingencies, using 55 196 cases (76% stable) from system operations studied on the Hydro Québec network providing 99.9% reliability.
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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 | 0.001 | 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 it