Securing Grid-Connected Packed E-Cell Multilevel Inverter: A LSTM-AE Approach to Hybrid Attack Mitigation
Bibliographic record
Abstract
The deployment of open communication infrastructure into power systems has drawn much attention due to its significant benefits, such as real-time monitoring, diagnostics, and regulatory purposes. But utilizing such technologies poses security challenges to the cyber-physical power systems (CPPS), which can highly degrade their operation. In this paper, a defense mechanism is adopted to tackle the hybrid attacks, including the Denial-of-Service (DoS) and false data injection (FDI) attacks in the grid-connected multilevel inverters from a systematic point of view. The proposed protection mechanism for the grid-connected multilevel inverter is realized in two stages. (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i>) A Long-Short Term Memory based on autoencoder (LSTM-AE) is developed to detect DoS attacks, and an event-trigger mechanism based on Lyapunov theory is implemented to eliminate the effect of false data. (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i>) A sliding mode observer is adopted to recognize FDI threats, where the false data is eliminated by injecting the negative value of the identified false data. A prototype of a grid-connected nine-level Packed E-Cell (PEC9) topology as a targeted multilevel inverter is constructed to experimentally validate the feasibility of the proposed cyber resilience scheme for CPPS in microgrid applications.
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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 | 0.001 | 0.001 |
| 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".