Robust Dynamic State Estimation of Power System With Measurement Outliers Based on Parameterized Analytical Cubature Kalman Filter
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Bibliographic record
Abstract
ABSTRACT Accurate state estimation is paramount for the smooth operation and management of power systems, significantly contributing to their safety, stability, and reliability. However, the presence of channel noise and outliers stemming from phasor measurement units renders as the noise model a deviation from the Gaussian distribution. To mitigate this challenge, this paper introduces a parameterized analytical update cubature Kalman filter (PACKF) that significantly enhances estimation accuracy. Firstly, the updated analytical form of the state variable is derived, in which an unknown parameter is introduced. Secondly, the unknown parameter is approximated using fixed‐point iteration, followed by the analytical computation of the required joint posterior probability density function (PDF). Finally, extensive simulations are conducted on the IEEE 39‐bus test system, indicating that the proposed method commendable accuracy and efficiency across diverse scenarios.
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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) | 0.000 | 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