Parallel-in-Time Object-Oriented Electromagnetic Transient Simulation of Power Systems
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
Parallel-in-time methods are emerging to accelerate the solution of time-consuming problems in different research fields. However, the complexity of power system component models brings challenges to realize the parallel-in-time power system electromagnetic transient (EMT) simulation, including the traveling wave transmission lines. This paper proposes a system-level parallel-in-time EMT simulation method based on traditional nodal analysis and the Parareal algorithm. A new interpretation scheme is proposed to solve the transmission line convergence problem. To integrate different kinds of traditional EMT models, a component-based EMT system solver architecture is proposed to address the increasing model complexity. An object-oriented C++ implementation is proposed to realize the parallel-in-time Parareal algorithm based on the proposed architecture. The results on the IEEE-118 test system show 2.30x speed-up compared to the sequential algorithm under the same accuracy with 6 CPU threads, and a high parallel efficiency around 40%. The performance comparison of various IEEE test cases shows that the system's time-domain characteristics determine the speed-up of Parareal algorithm, and the delays in transmission lines significantly affect the performance of parallel-in-time power system EMT simulations.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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