Quantum Variational Circuits for Detection of False Data Injection Against Power Transformers
Bibliographic record
Abstract
In smart grids, differential current relays protect power transformers by comparing local and remote measurements communicated over substation networks. However, this reliance on communication makes them vulnerable to false data injection attacks (FDIAs), leading to false tripping of the protected transformer and possibly system instability. This paper proposes a novel, quantum-based, data-driven scheme for detecting FDIAs targeting transformer relays. The proposed approach utilizes quantum variational circuits (QVCs) to analyze relay measurements, accurately distinguishing between malicious measurements and those associated with genuine fault conditions. The proposed scheme is trained and tested under various FDIA and fault scenarios generated in an OPAL-RT environment. Our results demonstrate that the proposed QVC-based scheme accurately detects FDIAs, maintains relay dependability, and outperforms existing solutions. The proposed QVC scheme is also validated using an OPAL-RT Hardware-In-the-Loop real-time simulation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".