Resilient Model Predictive Adaptive Control of Networked Z-Source Inverters Using GMDH
Bibliographic record
Abstract
Power grids are increasingly evolving into more efficient smart grids thanks to advancements in information technology. However, these intelligent communication-dependant power systems are becoming more susceptible to cyberattacks than their traditional peers. Power inverters are one of the main network connected devices supporting grid stability, which can potentially be targets for cyber-threats. This article presents an attack-resilient model predictive adaptive controller for a class of inverters, called Z-source inverters, to protect them against deception attacks. The proposed control structure includes a model predictive controller equipped with an unknown input Kalman filter and an estimator for system perturbations. The group method of data handling technique is used to estimate system uncertainties and make the system robust against perturbations. The unknown input Kalman filter is also adopted to estimate the states and unknown inputs in the presence of noisy measurements and cyberattacks on the control signal. We mathematically prove that in the presence of noise and perturbations, the proposed controller guarantees the stability of the system under a deception attack, which causes a delay, less than a sampling time, in control signals. Simulation results reveal the effectiveness of the presented controller in protecting the system against pulse, scaling and random attacks in the presence of system uncertainties, source and load fluctuations and output noises.
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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".