Resilient Two Degree-of-Freedom IMC for Time Delay Attack Mitigation of Interconnected Power System
Bibliographic record
Abstract
Cross-boundary power systems are more susceptible to cyberattacks, especially time-delay attacks (TDAs), which can disrupt system functioning by delaying communications. This paper offers a resilient two-degree-of-freedom internal model control (2DOF-IMC) strategy to reduce the negative impact of TDA on power system stability. This controller provides an exceptional level of disturbance rejection and reference tracking under attack conditions. To design the proposed controller, the higher-order plant dynamics are reduced by Hankel-based model order reduction approach. The effectiveness of the proposed 2DOF-IMC method is demonstrated via simulation experiments on an interconnected power system for different TDA scenarios. Comparison with conventional IMC (C-IMC) reveals advanced strength and improved frequency control mechanism. The developed framework reduces time delay destabilizing influences, guaranteeing proper functioning of the system during hostile cyber-attack events. The evidence supports the use of resilient control technologies for defending modern power grids from these TDAs. This research provides a significant step toward enhancing the security and stability of interconnected power systems in the face of evolving cyberattack landscapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".