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Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems

机译:基于生成的侵犯网络的基于网络的网络 - 物理电力系统故障

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摘要

This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.
机译:本文提出了一种用于分布式电力系统的新型诊断框架,其基于使用生成的对抗网络来产生电网中的人工净化。所提出的框架利用了包括从网络物理电力系统中的每个总线收集的电压,频率和相位角的原始数据测量。首先将收集的测量馈入特征选择模块,其中已经用于从初始可用功能集中提取最新的技术的最新技术。所选择的特征被输入到截止生成模块,其中采用生成的对抗网络来生成所选特征的相应截止。然后将所产生的倒置馈入分类模块,其中用于故障诊断的两个不同的分类模型中使用。多次实验旨在探讨噪声,故障值和采样率对所提出的框架性能的影响。通过对IEEE 118总线系统的全面研究验证了拟议框架的有效性。

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