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Fast sequence component analysis for attack detection in smart grid

机译:快速序列成分分析,用于智能电网中的攻击检测

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Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of “if” but a matter of “when” in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.
机译:现代电力系统已开始将同步相器技术集成到日常操作中。鉴于提供的解决方案数量和应用程序开发的成熟度,关于这些技术在世界各地的控制中心中无处不在,不是“如果”的问题,而是“何时”的问题。尽管好处很多,但通过伪装成真实测量值的欺骗性数据信号,可以轻松取消操作员级应用程序的功能。这种欺骗行为是邪恶的(通常是恶意的)行为的常见先兆。提出了相关系数表征和机器学习方法来检测和识别欺骗数据信号的注入。所提出的方法利用了电力系统参数固有的统计关系,对其进行了量化和表示。已经开发了几种欺骗方案来定性和定量地证明检测能力。

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