A Dynamic Programming Methodology to Develop De-Icing Strategies During Ice Storms by Channeling Load Currents in Transmission Networks
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Bibliographic record
Abstract
The ice storm of 1998 in northeastern North America caused much damage to the electrical installations of Trans-E/spl acute/nergie, the transmission provider in Que/spl acute/bec. Consequently, staff there and at Hydro-Que/spl acute/bec's research centre IREQ have deployed substantial efforts to mitigate the effects of future ice storms. This paper describes one of many projects dedicated to that goal. A computer program has been developed to simulate the ice buildup (also called accretion) on wires of electrical transmission lines and the melting of this ice using the heat generated by the currents flowing in the wires, under evolving network and weather conditions. Using these simulation tools, scenarios presenting different network configurations can be tested and the best sequence of scenarios, called a de-icing strategy, can be applied to reduce the ice buildup over the network. The methodology proposed here to determine de-icing strategies, based on dynamic programming, constitutes an optimal control to minimize ice buildup on the network over the set of scenarios and over the time horizon spanning the anticipated duration of the ice storm. A prototype of this computation has been tested using network data from the TranE/spl acute/nergie network and weather data from the 1998 ice storm. Once completed, the program will serve as an aide to operators during ice storms and as a training tool to better prepare for such eventualities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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