Resilient Dynamic State Estimation for Power System Using Cauchy-Kernel-Based Maximum Correntropy Cubature Kalman Filter
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Bibliographic record
Abstract
Accurate estimation of dynamic states is the key to monitoring power system operating conditions and controlling transient stability. The inevitable non-Gaussian noise and randomly occurring denial-of-service (DoS) attacks may, however, deteriorate the performance of standard filters seriously. To deal with these issues, a novel resilient cubature Kalman filter based on the Cauchy kernel maximum correntropy (CKMC) optimal criterion approach (termed CKMC-CKF) is developed, in which the Cauchy kernel function is used to describe the distance between vectors. Specifically, the errors of state and measurement in the cost function are unified by a statistical linearization technique, and the optimal estimated state is acquired by the fixed-point iteration method. Because of the salient thick-tailed feature and the insensitivity to the kernel bandwidth (KB) of Cauchy kernel function, the proposed CKMC-CKF can effectively mitigate the adverse effect of non-Gaussian noise and DoS attacks with better numerical stability. Finally, the efficacy of the proposed method is demonstrated on the standard IEEE 39-bus system under various abnormal conditions. Compared with standard cubature Kalman filter (CKF) and maximum correntropy criterion CKF (MCC-CKF), the proposed algorithm reveals better estimation accuracy and stronger resilience.
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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