Model-Free Resilient Grid-Forming and Grid-Following Inverter Control Against Cyberattacks Using Reinforcement Learning
Bibliographic record
Abstract
The U.S. movement toward clean energy generation has increased the number of installed inverter-based resources (IBR) in the grid, introducing new challenges in IBR control and cybersecurity. IBRs receive their set point through the communication link, which may expose them to cyber threats. Previous work has developed various techniques to detect and mitigate cyberattacks on IBRs, developing schemes for new inverters being installed in the grid. This work focuses on developing model-free control techniques for already installed IBR in the grid without the need to access IBR internal control parameters. The proposed method is tested for both the grid-forming and grid-following inverter control. Different detection and mitigation algorithms are used to enhance the accuracy of the proposed method. The proposed method is tested using the modified CIGRE 14-bus North American grid with seven IBRs in PSCAD/EMTDC. Finally, the performance of the detection algorithm is tested under grid normal transients, such as set point change, load change, and short-circuit fault, to make sure the proposed detection method does not provide false positives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".