Adaptive Resilient Control Against False Data Injection Attacks for a Multi-Energy Microgrid Integrating Power and Hydrogen Energy Systems Using Deep Reinforcement Learning
Bibliographic record
Abstract
Due to the benefits of using hydrogen energy, such as enhanced system reliability, reduced CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, and long term sustainability, the integration of hydrogen energy systems with power system sinmulti-energy microgrids (MEMGs) has attracted increasing research interest. The wide spread adoption of information and communication technologies has also increased the risk of cyber-attack son MEMGs. False data injection attacks (FDIAs), a common type of cyber attack, could falsify energy arbitrage commands, there by affecting these condary frequency regulation and degrading the power quality of the MEMG. These attacks not only affect the normal operations of the MEMG but also reduce its economic profit. To address these issues, this paper proposes a novel deep reinforcement learning-based adaptive resilient control scheme to coordinate the energy conversion between electricity and hydrogen to eliminate the frequency deviation and maximize daily total revenue. Unlike traditional optimization techniques, the propos d deep reinforcement learning-based approach enables real-time adaption to dynamic and adversarial conditions, allowing the system to learn optimal strategies and improve resilience against FDIAs. Case studies withreal-world data sets showthat as theattack injection level increasesfrom5%to20%, the proposed method maintains zero frequency deviation, with the total revenue decreasing by only 1.8%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".