Cyber Vulnerability Analysis and Anomaly Detection in a DC Microgrid Cluster
Bibliographic record
Abstract
DC microgrids have been gaining significant attention due to their numerous advantages over traditional AC power systems. However, maintaining a stable DC bus voltage remains a key challenge, mainly due to the variable power output of distributed energy resources (DERs) and the unpredictable nature of load demand. To overcome this challenge and enhance system reliability, multiple microgrids are interconnected through parallel interlinking converters (ICs) to form a DC microgrid cluster. In this system, the ICs are governed by a hierarchical control strategy that enables proportional power sharing among microgrids without relying on a central controller. However, the use of distributed control and interconnectivity increases the risk of cyberattacks, as communication protocols commonly employed in such systems–such as IEC 61850 and IEEE 2030.5–often lack robust security measures. This paper analyzes existing vulnerabilities in the system and designs a zero-sum false data injection attack (FDIA) that overloads the ICs without altering microgrids power generation and DC bus voltage. Then, a attention-based long short-term memory (Att-LSTM) model is designed to detect the attack and trigger early-stage alarms. The results demonstrate that the proposed Att-LSTM detection method successfully distinguished between attack and normal scenarios, and also outperformed other commonly used classification techniques.
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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 | 0.001 | 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".