Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding
Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while practical deployment further requires reliable performance under realistic computational constraints. This paper presents a simulation-driven modeling framework for dissolved gas analysis-based transformer fault diagnosis, in which a carefully engineered variational quantum classifier (VQC) is employed as the computational core and systematically analyzed through simulation. The framework integrates domain-aware feature modeling derived from Duval geometry with a lightweight two-qubit quantum representation, enabling nonlinear gas-interacti...
| Authors | Huy Hoang Le, Ba Tu Phung, Dai Huynh et al. |
|---|---|
| Primary category | quant-ph |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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