Robust Dynamic State Estimation for Power System Based on Adaptive Cubature Kalman Filter With Generalized Correntropy Loss
Bibliographic record
Abstract
Due to the unfavorable interference of non-Gaussian noise, abnormal system states, and rough measurement errors, dynamic state estimation (DSE) plays an important role in the safe operation of power system. A novel DSE method based on an adaptive cubature Kalman filter (CKF) with generalized correntropy loss (GCL) criterion, termed AGCLCKF, is developed to deal with the complex non-Gaussian distribution noises of power system in this paper. First, a nonlinear regression model is derived to simultaneously incorporate the state and noise errors into the GCL cost function, and a fixed-point iteration is exploited to recursively update the posterior state estimate. Then, considering that the filtering performance of the estimator is largely determined by the kernel bandwidth in correntropy, an adaptive factor is established to adjust the kernel bandwidth of kernel function in real-time, which can improve the flexibility and accuracy of dynamic state estimation in the existence of bad measurement information. Finally, extensive simulation results carried out on the IEEE 39-bus test system demonstrate that the proposed method can achieve much accuracy and robustness under various situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".