量子计算 (QC) 和深度学习已显示出支持变革性进步的前景，并且最近在广泛的领域中广受欢迎。本文提出了一种基于混合 QC 的电力系统故障诊断深度学习框架，该框架将条件受限玻尔兹曼机的特征提取能力与深度网络的有效分类相结合。基于 QC 的训练方法克服了此类深度学习模型的复杂性所带来的计算挑战，该方法有效地利用了量子辅助学习和经典训练技术的互补优势。提出的基于混合 QC 的深度学习框架在模拟电力系统上进行了测试，该系统具有 30 条总线和变电站和输电线路故障的广泛变化，展示框架的适用性、效率和泛化能力。就所需的计算工作量和诊断性能的质量而言，所提出的混合方法享有高计算效率，而不是经典训练方法。此外，基于人工神经网络 (ANN) 和决策树 (DT) 的最先进模式识别方法实现了具有更快响应时间的卓越可靠的故障诊断性能。
Quantum computing (QC) and deep learning have shown promise of supporting transformative advances and have recently gained popularity in a wide range of areas. This paper proposes a hybrid QC-based deep learning framework for fault diagnosis of electrical power systems that combine the feature extraction capabilities of conditional restricted Boltzmann machine with an efficient classification of deep networks. Computational challenges stemming from the complexities of such deep learning models are overcome by QC-based training methodologies that effectively leverage the complementary strengths of quantum assisted learning and classical training techniques. The proposed hybrid QC-based deep learning framework is tested on a simulated electrical power system with 30 buses and wide variations of substation and transmission line faults, to demonstrate the framework’s applicability, efficiency, and generalization capabilities. High computational efficiency is enjoyed by the proposed hybrid approach in terms of computational effort required and quality of diagnosis performance over classical training methods. In addition, superior and reliable fault diagnosis performance with faster response time is achieved over state-of-the-art pattern recognition methods based on artificial neural networks (ANN) and decision trees (DT).
