Support Vector Machine-Based False Data Injection Attacks Detection in Interconnected DC Microgrids
Bibliographic record
Abstract
The integration of multiple DC microgrids offers a robust solution for modern energy needs, especially with the high penetration of intermittent renewable sources. However, the reliance on extensive communication networks for coordinating Multiple interlinking converters (MICs) in these microgrids introduces vulnerabilities, particularly to False Data Injection Attacks (FDIAs), which can compromise system stability and security. This paper presents an AI-based anomaly detection strategy utilizing Support Vector Machines (SVM) at the primary control level to detect FDIAs in clustered DC microgrids. The proposed SVM model, trained offline with a comprehensive dataset of operational and attack scenarios, effectively delineates boundaries between normal and attack-induced conditions, enabling real-time detection in the online phase. Upon identifying an FDIA, the system decouples the primary control and transitions to a localized power balancing control, effectively preserving microgrid stability. Extensive simulations validate the efficacy of the proposed approach, demonstrating its ability to sustain stable microgrid operation under various FDIA scenarios, including time-varying and unbounded attacks.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".