变压器故障诊断：基于高效仿真驱动的变分量子分类器与领域感知特征编码
Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding
- The University of Danang - University of Science and Technology（岘港大学理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对变压器故障诊断中数据稀缺与噪声挑战，提出基于杜瓦尔几何特征编码和双量子比特变分量子分类器的仿真驱动框架，以极少量子资源实现高精度、强泛化的诊断。
AI中文摘要：
早期变压器故障诊断面临非线性溶解气体相互作用、故障特征重叠以及标记数据有限等挑战，而实际部署进一步要求在现实计算约束下具备可靠性能。本文提出了一种基于仿真驱动的建模框架，用于基于溶解气体分析的变压器故障诊断，其中精心设计的变分量子分类器（VQC）作为计算核心，并通过仿真进行系统分析。该框架将源自杜瓦尔几何的领域感知特征建模与轻量级双量子比特量子表示相结合，能够在浅层参数化电路中捕获非线性气体相互作用效应。设计了混合ZX-YY量子特征映射以建模非对易特征相互作用，而全纠缠的EfficientSU2拟设则在严格资源限制下提供足够的表达能力。通过包含噪声感知电路仿真、跨数据集验证和有限硬件在环执行的综合仿真流程评估模型行为，从而检验电路深度、噪声和优化策略的关键影响。在基准溶解气体分析数据集上的仿真结果表明，该方法以极少的量子资源实现了高诊断精度、强泛化能力和对现实噪声水平的鲁棒性。结果凸显了仿真知情建模在实际变压器诊断应用中的有效性，为评估量子增强故障诊断方法提供了一条可复现且资源高效的路径。
英文摘要：
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-interaction effects to be captured within a shallow parameterized circuit. A hybrid ZX-YY quantum feature map is designed to model non-commuting feature interactions, while a full-entanglement EfficientSU2 ansatz provides adequate expressive capacity under strict resource limits. Model behavior is evaluated using a comprehensive simulation pipeline including noise-aware circuit emulation, cross-dataset validation, and limited hardware-in-the-loop execution, allowing key effects of circuit depth, noise, and optimization strategy to be examined. Simulation results on benchmark dissolved-gas-analysis datasets demonstrate high diagnostic accuracy, strong generalization capability, and robustness to realistic noise levels with minimal quantum resources. The results highlight the effectiveness of simulation-informed modeling for practical transformer diagnostic applications, offering a reproducible and resource-efficient pathway for evaluating quantum-enhanced fault diagnosis methods.
