Development of Rule-Based Classifiers for Rapid Stability Assessment of Wide-Area Post-Disturbance Records
Bibliographic record
Abstract
The paper proposes a systematic scheme for building compact and transparent fuzzy rule-based classifiers for rapid stability assessment; the classifiers are initialized by large accurate decision trees (DTs). The approach starts by selecting strategic monitoring buses where phasor measurement units (PMUs) are placed to capture wide-area response signals in real-time operation. These measurements are processed in the 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. These so-called wide-area severity indices (WASI) are reliable time-varying stability indicators that form the basis of an effective classification system. Large-size DTs are used to generate initial accurate classification boundaries for decision making as early as 1 s or 2 s after fault clearing. From the DT classification boundaries, fuzzy membership functions (MFs) are developed and the corresponding fuzzy rule base is formulated parsimoniously by eliminating redundant MFs and rules using a similarity measure. The resulting fuzzy-rule classifiers are successfully tested for system-wise and area-wise contingencies based on a large database of detailed simulations of the Hydro-Quebec grid and are further confirmed on actual measurements recorded with existing wide-area measurements (WAMS).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".