Topology information based decision trees to predict dynamic transfer limits and their sensitivities for Hydro-Quebec's network
Bibliographic record
Abstract
This paper presents a method, using data mining techniques, to find dynamic power transfer limits and sensitivity of the limits due to element variations for the Hydro-Quebec power system based on topology information. The paper illustrates a systematic way to automatically generate a tremendous amount of cases to represent a wide range of parameter variations and compute their corresponding transfer limits based on time-domain dynamic simulations. These transfer limits are used to determine the transfer limit sensitivity for each parameter. Data mining techniques are used to build regression trees on this huge database, generated by the supercomputer at IREQ (the Hydro-Quebec Research institute), to find the relationship between either the transfer limits or the Δlimits and parameter variations. The benefit of this method is its ability to determine limits and/or Δlimits without requiring time-domain simulations in future studies. Rapid access to these limits and the sensitivities of the status of specific elements can potentially be foreseen to aid planning engineers in the planning and execution steps of limit calculations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".