Robust Defense Framework for Active Distribution Networks Under Multiple Attacks
Bibliographic record
Abstract
This article proposes a robust defense framework for hierarchical active distribution networks under multiple malicious attacks including false data injections (FDIs) and line disconnections. The framework integrates an adaptive augmented observer and a Defender-Attacker-Optimizer (D-A-O) algorithm against different layers of attacks. The adaptive augmented observer reconstructs the FDI signals and sends them to the controller as compensation values to defend against controller FDI attacks. In addition, the observer transmits the observed system states which are used in the D-A-O algorithm. Due to the observed true states of the system, the observer can defend against interlayer FDI attacks from the physical layer to the cloud decision layer. The D-A-O algorithm, based on Column-and-Constraint Generation (CC&G), is designed to achieve lower cost of the system under line disconnection scenarios. The proposed framework demonstrates stronger attack defense and lower cost through simulation and hardware-in-the-loop (HIL) testing of an IEEE 33-bus system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".