一种基于量子神经网络的电能质量扰动检测与识别方法
A Quantum Neural Network-Based Approach to Power Quality Disturbances Detection and Recognition
AI总结：
针对电能质量扰动检测，本文提出一种改进的量子神经网络模型，通过构建含数据与辅助量子比特的量子电路提取特征并分类，实验证明其在多类扰动识别中具有高准确率、强抗噪能力和低参数需求。
摘要：
电能质量扰动（PQDs）严重影响电力系统的稳定性和可靠性，需要准确高效的检测与识别方法。尽管许多用于PQDs检测与识别的经典算法已被广泛研究与应用，但量子领域的相关工作仍处于起步阶段。本文提出了一种用于PQDs检测与识别的改进型量子神经网络（QNN）模型。具体而言，该模型构建了一个包含数据量子比特和辅助量子比特的量子电路。通过编码层将经典数据嵌入数据量子比特，从而将其转换为量子数据。随后，利用参数化量子门形成变分层，促进量子比特信息变换，从而提取用于检测和识别的关键特征信息。通过测量辅助量子比特获得期望值，基于该期望值完成扰动分类。分析表明，QNN的运行时间和空间复杂度分别为$O\left ( poly\left ( N \right ) \right )$和$O\left ( N \right )$。大量实验验证了所提模型在PQD检测与识别中的可行性与优越性。在涉及扰动检测、七种单一扰动识别和十种混合扰动识别的实验中，该模型的准确率分别达到99.75%、97.85%和95.5%。此外，噪声模拟和对比实验表明，所提模型展现出强大的抗噪能力，仅需少量训练参数，并能保持高准确率。
英文摘要：
Power quality disturbances (PQDs) significantly impact the stability and reliability of power systems, necessitating accurate and efficient detection and recognition methods. While numerous classical algorithms for PQDs detection and recognition have been extensively studied and applied, related work in the quantum domain is still in its infancy. In this paper, an improved quantum neural networks (QNN) model for PQDs detection and recognition is proposed. Specifically, the model constructs a quantum circuit comprising data qubits and ancilla qubits. Classical data is transformed into quantum data by embedding it into data qubits via the encoding layer. Subsequently, parametric quantum gates are utilized to form the variational layer, which facilitates qubit information transformation, thereby extracting essential feature information for detection and recognition. The expected value is obtained by measuring ancilla qubits, enabling the completion of disturbance classification based on this expected value. An analysis reveals that the runtime and space complexities of the QNN are $O\left ( poly\left ( N \right ) \right )$ and $O\left ( N \right )$, respectively. Extensive experiments validate the feasibility and superiority of the proposed model in PQD detection and recognition. The model achieves accuracies of 99.75\%, 97.85\% and 95.5\% in experiments involving the detection of disturbances, recognition of seven single disturbances, and recognition of ten mixed disturbances, respectively. Additionally, noise simulation and comparative experiments demonstrate that the proposed model exhibits robust anti-noise capabilities, requires few training parameters, and maintains high accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
