Parallel-in-Time Object-Oriented Electromagnetic Transient Simulation of Power Systems
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 speedup 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.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".