Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements
Bibliographic record
Abstract
With the increasing demand for power system stability and resilience, effective real-time tracking plays a crucial role in smart grid synchronization.However, most studies have focused on measurement noise, while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem, a resilient fault-tolerant extended Kalman filter (RFTEKF) is proposed to track voltage amplitude, voltage phase angle and frequency dynamically.First, a threephase unbalanced network's positive sequence fast estimation model is established.Then, the loss phenomenon of measurements occurs randomly, and the randomness of data loss's randomness is defined by discrete interval distribution [0, 1].Subsequently, a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally, extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter (EKF).
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".