Fuzzy Partitioning of a Real Power System for Dynamic Vulnerability Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, the authors proposed a clustering approach based on the fuzzy C-medoid algorithm (FCMdd), for segregating large power systems into coherent electric areas centered around a representative so-called medoid-bus. This bus was shown to be a natural location for PMU in the context of wide-area measurement system (WAMS) configuration for of dynamic vulnerability assessment (DVA). The method was demonstrated on two test systems. The goal of this companion paper is to extend the approach to an actual grid (Hydro-Quebec) with more realistic characteristics in terms of geography and system dynamics. We start by developing a formulation of the coherency matrix that is recursive in time to enable online grid partitioning. The FCMdd is then implemented and compared with other statistical learning techniques. It is observed that only FCMdd is able to provide an intuitively appealing 7-clusters solution for 429-bus system. It is further demonstrated that medoids-based system-wise indices can forecast the contingencies severity under varying network configurations and loadings.
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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) | 0.001 | 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