Quantum Neural Network Parameter Estimation for Photovoltaic Fault Detection

Glen S. Uehara, Sunil Rao, Mathew Dobson, C. Tepedelenlioğlu, A. Spanias
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引用次数: 13

Abstract

In this paper, we describe solar array monitoring using various machine learning methods including neural networks. We study fault detection using a quantum computer system and compare against results with a classical computer. We specifically propose a quantum circuit for a neural network implementation for Photovoltaic (PV) fault detection. The quantum circuit is designed for two qubits. Results and comparisons are presented for PV fault detection using a classical and quantum implementation of neural networks. In addition, simulations of a Quantum Neural Network are carried for a different number of qubits and results are presented for PV fault detection.
光伏故障检测中的量子神经网络参数估计
在本文中,我们描述了使用包括神经网络在内的各种机器学习方法的太阳能电池阵监测。我们使用量子计算机系统研究故障检测,并将结果与经典计算机进行比较。我们特别提出了一种量子电路用于光伏(PV)故障检测的神经网络实现。量子电路是为两个量子位设计的。给出了用经典神经网络和量子神经网络进行光伏故障检测的结果和比较。此外,对量子神经网络进行了不同量子位数的模拟,并给出了用于PV故障检测的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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