Event-Triggered Entropy Learning for Encountering of FDI Attack in Grid-Connected Packed E-Cell Inverter
Bibliographic record
Abstract
With the high penetration of cyber-physical systems, the power electronic interfaces in smart grids (SGs) are threatened by cyber-attacks. False data injection (FDI) attacks are one of the most repetitive cyber threats that can adversely affect the performance of grid-connected multilevel inverters by manipulating the sensor data in the communication links. In particular, this brief focuses on the design of an event-triggering security control mechanism against cyber-attacks in a grid-connected nine-level packed e-cell (PEC9) inverter in two stages. (I) An adaptive detection is designed by incorporating based on high-order extended state observer (HOESO) and entropy learning to predict the system output. (II) An event trigger mechanism is established to block the false data injected into the measurement signals and eliminate it using the feedback controller. By training the capability of deep neural networks (DNNs), the coefficients embedded in the HOESO are designed to obtain an accurate estimation. By constructing a laboratory prototype of the grid-connected PEC9 inverter, experimental examinations under various types of FDI attacks, including manipulating the current signal using pulse and sinusoidal false data are carried out to verify the resilience of the suggested defense mechanism. Experimental outcomes of PEC9 reveal that the suggested scheme can effectively recognize and mitigate the effect of cyber threats, ensuring the security and reliability of power inverters in SG applications.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".