Fast Contingency Filtering Using Machine Learning for Power System Planning
Why this work is in the frame
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Bibliographic record
Abstract
Modern power systems assess reliability $N -k$ and $k =\{ 2, 3, 4,\ldots\}$ criteria to guarantee the secure, sustainable and optimal operation of power networks. However, performing these studies with traditional methods is computation-intensive and time-prohibitive for the long-term planning of large networks. In this paper, we present a Machine Learning (ML) model for rapid contingency filtering in order to evaluate problematic $N - k$ contingency scenarios. Our proposed model is trained with data sets stochastically created and labeled with AC load flows considering load forecasting. Their inputs are the time and $N - \quad k$ status of network equipment. The performance of the proposed ML model was evaluated on the IEEE 39-Bus System for a planning period of 10yr with a time resolution of 1h. The performance obtained was an accuracy greater than 95% with a time acceleration of approx. 2500x. This result makes the proposed model suitable for supporting decision making during the planning of power systems.
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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 | 0.000 | 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