Mitigating False Data Injection Attacks on Inverter Set Points in a 100% Inverter-Based Microgrid
Bibliographic record
Abstract
The increasing number of 100% inverter-based microgrids is introducing new challenges in their control and cybersecurity. Previous work has studied the cyber vulnerabilities of microgrids; however, very few work has studied methods to mitigate and detect cyberattacks in a 100% inverter-based microgrid. Attackers can utilize communication-based devices in a microgrid to launch false data injection (FDI) attacks and cause voltage and frequency instability. This paper studies the effects of FDI attacks on the real and reactive power set points of inverter-based resources (IBR) in a 100% inverter-based microgrid. This work co-simulates a power system using PSCAD and a communication system using Python to study FDI attacks. The communication system is modeled as a first in first out (FIFO) queue model. A long short-term memory (LSTM)based method is used to mitigate and detect ramp and bias FDI attacks. The proposed strategy is tested on a microgrid with four IBRs subject to different FDI 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".