MPI parallel computing on eigensystems of small signal stability analysis for large interconnected power grids
Bibliographic record
Abstract
Eigenanalysis is widely used in power system stability study. With PC technologies available today, it takes long time to compute the entire eigensystems of large interconnected power grids. Since power transmission lines are connected & disconnected and line loads keep changing frequently, tracking eigensystems in real-time requires parallel computation. Recently, a parallel eigensystem computation method, the Break and Bind (B & B) method, has been proposed by Dr. H. M. Banakar in McGill University. This method is viewing connection of two isolated sub-networks as being equivalent to a rank-one modification (ROM) of the stiffness matrix and considering the two sub-networks as a single entity. Research of this thesis consists of implementing the B & B method based on Message Passing Interface (MPI) parallel programming in #C. The developed MPI codes were executed on super-computers - Krylov cluster of CLUMEQ and Mammouth Series II cluster of RQCHP. The testing results have demonstrated that the eigensystem of a power system composed of around 4,000 generators can be updated within two seconds.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".