Resilient Ratio Control Assisted Virtual Inertia for Frequency Regulation of Hybrid Power System Under DoS Attack and Communication Delay
Bibliographic record
Abstract
Due to the presence of Denial-of-Service (DoS) attacks and communication time delay (CTD), frequency regulation of a thermal and wind plants-based hybrid power system (HPS) with wind power fluctuations and load variations may not be guaranteed, and in the worst situation overall system may be destabilized. This paper addresses a robust proportional-integral (PI) controller to compensate for the impact of CTD and for the first time, an intelligent fuzzy logic-assisted ratio control-based virtual inertia (VI) is implemented for handling the DoS attacks. The controller's design is carried out using physics-inspired optimization called Fick's law optimization (FLO), where Kharitonov's theorem is used to obtain the maximum and minimum bounds of the controller. The effectiveness of proposed control design is assessed by considering the various practical scenarios such as cyber-attacks, parametric uncertainties, varying CTD, stochastic wind power fluctuations, industrial and domestic loads, step load perturbations and the presence of system nonlinearities such as generator dead band (GDB), valve limits and generator rate constraint (GRC). Moreover, for the stability assessment of the proposed control design, maximum sensitivity$( {{{{\bm{M}}}_{\bm{S}}}} )$based stability evaluation is employed. Finally, the controller performance is also evaluated upon a multi-machine interconnected IEEE-39 bus system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".