Faster-than-Real-Time Simulation of a Large Brazilian AC/DC Grid to Analyze Electromagnetic & Electromechanical Transients as Well as Commutation Failures
Bibliographic record
Abstract
Power system studies consist mainly of power flow, short circuit, electromechanical transient (stability EMS) and electromagnetic transient (EMT) studies. EMS simulations are usually performed at the planning stage and by system operators using phasor methods to accelerate the analysis of very large systems under several fault contingencies. EMT analysis is performed with very detailed models and small integration steps to simulate fast transients and control dynamics. Determining the risk of multiple HVDC commutation failures and the resulting power transfer capability of large multi-infeed HVDC inverter systems would normally require performing EMT simulation using detailed HVDC controller models. However, using traditional singleprocessor EMT software is normally not practical to analyze the EMS of large systems with several HVDCs due to the long calculation times. This paper analyzes the performance of parallel EMT software to analyze the transient stability of practical and large AC/DC systems using standard multi-core computers. It will be demonstrated that a simulation speed of two real-time speed can be achieved with a reasonable number of processors, even for a system as large as the Brazilian grid.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".