A message-passing distributed-memory parallel power flow algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a parallel direct linear solver based on a message-passing distributed-memory multiprocessor architecture such as a cluster of workstations. The results show that the new algorithm can achieve nearly linear speedup for two large-scale power system cases on a small cluster of GNU/Linux dual-processor workstations. The workstations are connected via 100 Mbit/s Ethernet, i.e., the parallel machine consists of hardware readily found in any engineering department. Based on the presented parallel direct linear solver, it is possible to parallelize totally the Newton power flow solution process. In addition, the METIS-based partitioning scheme can handle common control devices such as PV-PQ switching. Furthermore, by tuning the vertex and branch weights, the performance of the power flow solution can be optimized for the available hardware. For a workstation cluster on 100 Mbit/s Ethernet, the speedup appears to saturate beyond eight processors due to load imbalance and the aggregate growth of the partition separators. Nevertheless, the message-passing distributed-memory multiprocessor architecture can be used in other power system applications, such as state estimation and transient stability. Furthermore, an iterative linear solver could improve scalability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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