Distributed State Estimation in Multi-area DC MGs Experiencing Denial-of-Service Attacks
Bibliographic record
Abstract
Multi-area direct current (DC) microgrid (MG) is a promising platform to fully utilize pollution-free renewable energy sources and increase the reliability of the power utility operation. State estimation of such a system has some difficulties, as the system is multi-area and the information of the whole system is not metered centrally, which persuades considering distributed state estimation. Meanwhile, the effects of attacks on the communication links between the local estimators are indispensable. Therefore, this work proposes a robust distributed state estimation for interconnected multi-area DC MG power systems subjected to Denial-of-service (DoS) attacks. The designed estimator is a Kalman-type filter, which reduces the effect of the noisy measurements on the estimation and eliminates the effect of DoS attacks on the state estimation. A recursive algorithm is suggested to update the estimator gain online in such a way that the covariance of the estimation error is minimized theoretically. The proposed approach is applied to the effect of a DoS attack on t a five-area interconnected DC MG, for which five local distributed estimators are constructed to estimate each local area’s state vector. Comparative results are given to highlight the superiorities of the suggested approach.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".