A Dynamic Programming Methodology to Develop De-Icing Strategies During Ice Storms by Channeling Load Currents in Transmission Networks
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".