Adaptive multi-scale electrothermal model of REBCO coated conductors embedded in a commercial power system transient simulator
Bibliographic record
Abstract
Abstract High temperature superconducting coated conductors (CCs) are wires with ideal nonlinear properties for use as resistive superconducting fault current limiters (rSFCLs) for power systems. However, choosing a conductor architecture that can survive all types of excitations occurring in a specific power system is a big challenge. Firstly, the non-uniformity of the critical current along the length of commercial CCs makes them prone to hot spots. Secondly, the current flowing in a CC-based rSFCL is determined by the specific dynamics of the power system in which it is installed. In order to correctly design the CC architecture, one must be able to compute the electrothermal bevahiour of CCs submitted to realistic power system excitations. This requires coupling a physically-representative CC model with a suitable commercial power system simulator. In this paper, an example of such coupling is presented, with the particularity that the numerical method used to simulate CCs can account for realistic lengths used in rSFCLs (hundreds of meters of CCs), while still being able to track hot spot developments on the micrometer scale, thanks to a dynamic adaptive meshing strategy. The coupling is realized within the EMTP-RV environment through a dynamic link library that contains the physical CC model. A few examples of applications are presented, in which propagation of multiple hot spots triggered by the power system are simulated in very moderate computation times. Besides helping rSFCL manufacturers to better select a proper CC architecture, the proposed model can also be used in regular power system simulations as an accurate rSFCL device model, which is expected to greatly help power system engineers to better plan their integration in the grid.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".