Voltage collapse detection using ant colony optimization for smart grid applications
Why this work is in the frame
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Bibliographic record
Abstract
Load demand levels in power networks are continuing to increase and as a result bus voltages are requiring more support from generators elsewhere in the network in order to maintain the voltage within specified limits. If the network is unable to support the increasing demand, bus voltage will begin to degrade until the point of voltage collapse which can lead to catastrophic network failure. Previous studies have shown that evolutionary computing techniques are effective methodologies for locating voltage collapse points. Ant Colony Optimization techniques allow for the optimization of many independent parameters simultaneously, the loading parameter for each bus is considered in this work. In this study Ant Colony Optimization is applied to detect voltage collapse conditions in power networks, to obtain faster computing time with the future goal of providing online detection and prediction for use in smart grids. Results and conclusions drawn from this study are also presented.
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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