Distributed Secure Estimation Against Unknown FDI Attacks and Load Deviation in Multi-Area Power Systems
Why this work is in the frame
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Bibliographic record
Abstract
This brief studies the distributed estimation problem for multi-area power systems where the sensor measurements encounter with false data injection (FDI) attacks. For the large load frequency control (LFC) power systems with unknown load deviation, a distributed intermediate observer based method is proposed to estimate the system states, FDI attack signals and load deviation of each area in the power system. In this case, the gain of each intermediate observer is obtained by constructing a self-relative linear matrix inequality that can be easily solved by the standard software packages. Notice that the computational cost of each estimator is low even though the scale of power systems is large. Finally, the effectiveness of the proposed method is verified by an illustrative example.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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