GPU-Based DC Power Flow Analysis Using KLU Solver
Bibliographic record
Abstract
The Power Flow (PF) analysis is the backbone of power systems operation, scheduling, and planning studies. The main objective in PF analysis of power systems is to obtain the voltage magnitude and phase angle at each bus. Computational burden is a major concern in PF analysis since numerical methods, such as Newton-Raphson (NR) and Gauss-Seidel (GS) methods, are used to iteratively solve such a nonlinear problem. By increasing the size and complexity of power systems, the computational burden correspondingly increases. This paper aims at presenting a Graphics Processing Unit (GPU)-based Direct Current (DC) PF analysis using the KLU solver to minimize the computational burden. The presented method is a GPU-based sparse solver based on the LU decomposition and can be used for fast matrix calculation. The obtained results clearly show a speed-up of approximately$\times 10$compared to MATPOWER, which is broadly used for PF analysis of power systems.
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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 | 0.001 |
| 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) | 0.008 | 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 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".