A Cyber Attack Mitigation Scheme for Series Compensated DFIG-Based Wind Parks
Bibliographic record
Abstract
Subsynchronous Interaction (SSI) phenomenon is known to be one of the most frequent and severe stability issues of a Wind Park (WP), and can potentially lead to a significant loss of power generation. The broad impacts of this phenomenon on a power grid have made WPs interesting targets for cyber attacks. To initiate the SSI, an adversary can target either the power grid (external attacks) or the cyber system of WPs (internal attacks). This paper proposes a mitigation scheme for attacks that initiate the SSI phenomenon in series compensated doubly-fed induction generator (DFIG)-based WPs. External attacks are addressed by employing a robust static-output-feedback Subsynchronous Damping Controller (SSDC), which is designed based on the insensitive strip region and Linear Matrix Inequality (LMI) techniques. Internal attacks, however, are detected by comparing the estimated and measured converters’ currents. Once the compromised measurements are detected, the designed SSDC is restructured to mitigate the attacks. The effectiveness of the proposed method is demonstrated using detailed Electromagnetic Transient (EMT) simulations for both internal and external cyber attacks. Additionally, the performance of the proposed method is corroborated using a real-time co-simulation framework.
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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".