Mitigating False Data Injection Attacks in DC Microgrids with Multiple Interlinking Converters
Bibliographic record
Abstract
The coordination among Multiple Interlinking Converters (MICs) is facilitated through a low-bandwidth, neighborhood communication-assisted, distributed cooperative control strategy. However, these systems are vulnerable to False Data Injection Attacks (FDIAs), which create significant challenges by maliciously falsifying communication signals, disrupting the coordination of MICs, and potentially rendering the entire microgrid inoperable. To address this threat, this paper proposes a signal estimation stategy based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to mitigate FDIAs in MICs within clustered DC microgrids. During the offline training phase, the ANFIS-based estimator is developed using the local voltage measurements from each bidirectional interlinking converter as inputs and the sum of communicated signals entering each converter as the output. For the online stage, a reference tracking approach is developed to restore the attacked signals using the estimated values from ANFIS, effectively mitigating the impact of FDIA. Through extensive simulations, we demonstrate the effectiveness of our proposed approach in handling various FDIAs, including time-varying attack signals and unbounded attacks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".