This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON POWER SYSTEMS 1 Fuzzy Partitioning of a Real Power System for Dynamic Vulnerability Asses
Bibliographic record
Abstract
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 assess-ment (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-Québec) 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 ob-served 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 contingen-cies severity under varying network configurations and loadings. Index Terms—Coherency, coherent area, network partitioning, phasor measurement units, PMU placement, power system moni-toring, power system stability, vulnerability assessment, wide-area measurements. I.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.707 | 0.419 |
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".