Accelerated parallel WLS state estimation for large-scale power systems on GPU
Why this work is in the frame
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Bibliographic record
Abstract
Owing to the growing size and complexity of power networks, online monitoring of the power system state is a challenging computational problem. State estimation is paramount for reliable operation of large-scale power systems. Even with modern multi-core processors, the large number of computations in state estimation are still a burden on time and are highly memory intensive. In this paper the idea of using massively parallel graphic processing units (GPUs) for weighted least squares (WLS) based state estimation is introduced and executed. The GPU is especially designed for processing large data sets. A data-parallel implementation of the WLS method is proposed. The speed of the GPU-based state estimation for several test systems is compared with a sequential CPU-based program. The simulation results show a speed-up of 38 for a 4992-bus system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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 it