Stochastic Stability Analysis and Control of Secondary Frequency Regulation for Islanded Microgrids Under Random Denial of Service Attacks
Bibliographic record
Abstract
As communication networks are increasingly implemented to support the information exchange between microgrid control centers and/or local controllers, they expose microgrids to cyber-attack threats. This paper aims to analyze the stochastic stability of islanded microgrids in the presence of random denial of service (DoS) attack and propose a mode-dependent resilient controller to mitigate the influence of DoS attacks. Specifically, the small-signal model of the microgrid under the DoS attack is integrated as a stochastic jump system with state continuity disruptions. A new vulnerability metric is defined by using observability Gramians of the stochastic jump system, to measure the vulnerability of the system regarding DoS attack choices. The Lyapunov function analysis is conducted to find conditions sustaining the stochastic stability of the islanded microgrid in the form of linear matrix inequalities. A mode-dependent control approach is proposed for microgrids to mitigate the influence of random DoS attacks. In case studies, the vulnerability analysis and time-domain simulation results show the performance of the investigated microgrid can be degraded when the random DoS attacks exist. When the proposed mode-based secondary frequency controllers are installed, the islanded microgrid can sustain its stability during the attacking period and system dynamics rapidly converge when the DoS attack is over.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".