Análisis de Estabilidad Transitoria Utilizando el Concepto de Inercia y Minería de Datos
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
This work proposes a methodology to evaluate transient stability in power systems through time series clustering using the Dynamic Time Warping (DTW) metric. A script developed in Python integrates with DIgSILENT PowerFactory, allowing the extraction of rotor angles from each generator and referencing them to the Center of Inertia (COI). To obtain rotor angles, the study performs simulations on the New England 39-bus, 10-generator system in DIgSILENT PowerFactory, applying the unwrapping technique to correct discontinuities. Then, the methodology applies the K-means algorithm based on DTW to segment the generating units according to their transient response, identifying critical generators. However, DIgSILENT PowerFactory only provides plots of rotor angles referenced to a specific generating unit, which limits stability assessment. To overcome this restriction, this study implements the results directly into DIgSILENT PowerFactory, enabling the visualization of Python -processed graphs within the software environment. This integration enhances decision-making efficiency in power system operation and planning
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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