Cyber-Resilient Control for a 100% Inverter-Based Microgrid: Analysis and Real-Time Simulation
Bibliographic record
Abstract
As the U.S. moves toward cleaner electricity generation, the number of installed inverter-based resources such as wind and photovoltaic (PV) are on the rise. These inverter-based resources (IBR) rely on communication to update their real and reactive power set point; however, it opens room for cyberattacks. Previous work proposed ways to detect and mitigate cyberattacks for fully inverter-based microgrids using software simulation, but the feasibility of these methods needs to be tested using control hardware-in-the-loop (CHIL). This work develops a CHIL testbed using Typhoon HIL 402, Raspberry Pi 4, and a network switch to implement a cyber-resilient inverter control against false data injection (FDI) attacks. The Raspberry Pi 4 receives the electrical measurements from the Typhoon HIL using user datagram protocol (UDP) communication, runs the LSTM-based detection and mitigation code using the received measurements and sends back the corrected set point if an FDI attack is detected. The proposed method is tested on a fully inverter-based microgrid with four inverters under various scenarios.
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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 | 0.000 |
| 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".