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Online Pattern Recognition and Data Correction of PMU Data Under GPS Spoofing Attack

机译:GPS欺骗攻击下PMU数据的在线模式识别和数据校正

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摘要

Smart grids are increasingly dependent on data with the rapid development of communication and measurement. As one of the important data sources of smart grids, phasor measurement unit (PMU) is facing the high risk from attacks. Compared with cyber attacks, global position system (GPS) spoofing attacks (GSAs) are easier to implement because they can be exploited by portable devices, without the need to access the physical system. Therefore, this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference (PAD) deviation. Specifically, this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA, i.e., the step attack and the ramp attack. Then, the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed, which does not require the line parameters. After obtaining the estimated PAD deviations, the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations. Finally, with the case studies, the effectiveness of the proposed method is demonstrated, and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.
机译:智能电网越来越依赖于通信和测量的快速发展的数据。作为智能电网的重要数据源之一,Phasor测量单元(PMU)面临攻击的高风险。与网络攻击相比,全球位置系统(GPS)欺骗攻击(GSA)更容易实现,因为它们可以被便携式设备利用,而无需访问物理系统。因此,本文提出了一种新颖的GSA模式识别方法,并且所提出的方法的附加功能是对相角差(焊盘)偏差的数据校正。具体而言,本文分析了GSA对PMU测量的影响,并给出了两个GSA的常见模式,即步进攻击和斜坡攻击。然后,提出了借鉴GSA引入的传输线估计焊盘偏差的方法,其不需要线参数。在获得估计的焊盘偏差之后,可以根据估计的焊盘偏差的统计特征通过假设测试和相关系数来识别GSA的模式。最后,随着案例研究,对所提出的方法的有效性进行了说明,分析了模式识别的成功率和所提出的方法的在线性能。

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